From 175cb08f58e57b33ca3f540d75b9c3e81ce80aa2 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 28 Sep 2026 04:01:19 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: laion/ablation-pymethods2test-seqmean-arm0-15-8B Source: Original Platform --- .gitattributes | 42 + README.md | 35 + added_tokens.json | 28 + chat_template.jinja | 89 + config.json | 68 + generation_config.json | 12 + merges.txt | 151388 +++++++++++++++ model-00001-of-00004.safetensors | 3 + model-00002-of-00004.safetensors | 3 + model-00003-of-00004.safetensors | 3 + model-00004-of-00004.safetensors | 3 + model.safetensors.index.json | 407 + rl_config.json | 156 + special_tokens_map.json | 31 + tokenizer.json | 3 + tokenizer_config.json | 240 + .../20260612_220124_metrics_job_630124.csv | 29 + .../20260612_220124_metrics_job_630127.csv | 29 + .../20260612_220124_metrics_job_630130.csv | 15 + .../20260612_220124_metrics_job_661735.csv | 9 + .../20260612_220124_metrics_report.md | 393 + .../20260612_220124_metrics_table.csv | 79 + .../20260612_220124_reward_vs_steps.png | 3 + .../20260612_220124_trial_results.csv | 38124 ++++ ...0260612_220124_turn_count_distribution.png | 3 + ...0260612_220124_vllm_metrics_job_630124.csv | 8507 + ...0260612_220124_vllm_metrics_job_630127.csv | 8517 + ...0260612_220124_vllm_metrics_job_630130.csv | 7403 + ...0260612_220124_vllm_metrics_job_661734.csv | 1153 + ...0260612_220124_vllm_metrics_job_661735.csv | 3110 + .../20260612_220124_vllm_metrics_table.csv | 28686 +++ ...ion-pymethods2test-seqmean-arm0_630124.out | 3 + ...ion-pymethods2test-seqmean-arm0_630125.out | 1552 + ...ion-pymethods2test-seqmean-arm0_630126.out | 1665 + ...ion-pymethods2test-seqmean-arm0_630127.out | 3 + ...ion-pymethods2test-seqmean-arm0_630128.out | 370 + ...ion-pymethods2test-seqmean-arm0_630129.out | 2054 + ...ion-pymethods2test-seqmean-arm0_630130.out | 3 + ...ion-pymethods2test-seqmean-arm0_657667.out | 17 + ...ion-pymethods2test-seqmean-arm0_657668.out | 17 + ...ion-pymethods2test-seqmean-arm0_657669.out | 17 + ...ion-pymethods2test-seqmean-arm0_657670.out | 17 + ...ion-pymethods2test-seqmean-arm0_657671.out | 17 + ...ion-pymethods2test-seqmean-arm0_661733.out | 364 + ...ion-pymethods2test-seqmean-arm0_661734.out | 37567 ++++ ...ion-pymethods2test-seqmean-arm0_661735.out | 3 + vocab.json | 1 + 47 files changed, 292241 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 added_tokens.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 merges.txt create mode 100644 model-00001-of-00004.safetensors create mode 100644 model-00002-of-00004.safetensors create mode 100644 model-00003-of-00004.safetensors create mode 100644 model-00004-of-00004.safetensors create mode 100644 model.safetensors.index.json create mode 100644 rl_config.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 training_logs/20260612_220124_metrics_job_630124.csv create mode 100644 training_logs/20260612_220124_metrics_job_630127.csv create mode 100644 training_logs/20260612_220124_metrics_job_630130.csv create mode 100644 training_logs/20260612_220124_metrics_job_661735.csv create mode 100644 training_logs/20260612_220124_metrics_report.md create mode 100644 training_logs/20260612_220124_metrics_table.csv create mode 100644 training_logs/20260612_220124_reward_vs_steps.png create mode 100644 training_logs/20260612_220124_trial_results.csv create mode 100644 training_logs/20260612_220124_turn_count_distribution.png create mode 100644 training_logs/20260612_220124_vllm_metrics_job_630124.csv create mode 100644 training_logs/20260612_220124_vllm_metrics_job_630127.csv create mode 100644 training_logs/20260612_220124_vllm_metrics_job_630130.csv create mode 100644 training_logs/20260612_220124_vllm_metrics_job_661734.csv create mode 100644 training_logs/20260612_220124_vllm_metrics_job_661735.csv create mode 100644 training_logs/20260612_220124_vllm_metrics_table.csv create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630124.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630125.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630126.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630127.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630128.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630129.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_630130.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_657667.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_657668.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_657669.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_657670.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_657671.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_661733.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_661734.out create mode 100644 training_logs/ablation-pymethods2test-seqmean-arm0_661735.out create mode 100644 vocab.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a46e4bf --- /dev/null +++ b/.gitattributes @@ -0,0 +1,42 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +training_logs/ablation-pymethods2test-seqmean-arm0_630124.out filter=lfs diff=lfs merge=lfs -text +training_logs/ablation-pymethods2test-seqmean-arm0_630127.out filter=lfs diff=lfs merge=lfs -text +training_logs/ablation-pymethods2test-seqmean-arm0_630130.out filter=lfs diff=lfs merge=lfs -text +training_logs/ablation-pymethods2test-seqmean-arm0_661735.out filter=lfs diff=lfs merge=lfs -text +training_logs/20260612_220124_reward_vs_steps.png filter=lfs diff=lfs merge=lfs -text +training_logs/20260612_220124_turn_count_distribution.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..c79a824 --- /dev/null +++ b/README.md @@ -0,0 +1,35 @@ +--- +base_model: laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink +tags: +- rl +- skyrl +- agentic +- swe +library_name: transformers +--- + +# ablation-pymethods2test-seqmean-arm0-15-8B + +RL (SkyRL GRPO) checkpoint from the **sequence-mean / RLOO-n (arm0)** ablation of +the a3-successor study. The policy loss uses `loss_reduction=sequence_mean` with +the `advantage_estimator=rloo_n` (RLOO-n) estimator, contrasting with the +token-mean reduction of the a3 series. + +- **Base model:** [laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink](https://huggingface.co/laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink) (a Qwen3-8B SFT) +- **Training dataset:** [DCAgent/exp_rpt_pymethods2test-large](https://huggingface.co/datasets/DCAgent/exp_rpt_pymethods2test-large) +- **Checkpoint:** `global_step_15`. +- **Training:** SkyRL GRPO, `hf_save_interval=5`, 14x GH200 nodes on JSC Jupiter. + +The `rl_config.json` in this repo is the exact launch config used for reproducibility. +This is the step-15 checkpoint of the same run that produced +[laion/ablation-pymethods2test-seqmean-arm0-30-8B](https://huggingface.co/laion/ablation-pymethods2test-seqmean-arm0-30-8B) (step 30). + +## Training Traces + +Training-time Daytona/Harbor rollouts for this run are uploaded as +a companion dataset: +**[open-athena/ablation-pymethods2test-seqmean-arm0](https://huggingface.co/datasets/open-athena/ablation-pymethods2test-seqmean-arm0)** + +The dataset contains the `last` episode of each trial (per +`make_and_upload_trace_dataset --episodes last`) — the same rollouts +the policy was trained on after rollback / truncation. diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..b54f913 --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,28 @@ +{ + "": 151668, + "": 151658, + "": 151666, + "": 151667, + "": 151657, + "": 151665, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 151652 +} diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..59b4193 --- /dev/null +++ b/config.json @@ -0,0 +1,68 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 12288, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 36, + "model_type": "qwen3", + "num_attention_heads": 32, + "num_hidden_layers": 36, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 1000000, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "4.57.6", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 151936 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..9adbb28 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "4.57.6" +} diff --git a/merges.txt b/merges.txt new file mode 100644 index 0000000..3134955 --- /dev/null +++ b/merges.txt @@ -0,0 +1,151388 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. 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+ÙĬ ج +íĴ Ī +à¸ŀ à¸ļ +ห ัว +з на +ר ק +à¹Ĥ ร +Ġ×ij ס +ĠBaÅŁ kan +ĠëĶ ° +à¸Ń ัà¸Ļ +ีà¹Īย ว +н еÑģ +à¹Ģà¸Ķ ิà¸Ļ +ÙĬ اÙĨ +×ķ׾ ×Ļ +ا خت +צ ×ķת +ãģĵ ãģĵ +ĠاÙĦ اÙĨ +ĠпÑĢо ÑĨ +ãģ¾ ãģł +׼ ס +ĠاÙĦ Ø¢ +ÙĬ ز +ĠاÙĦد ÙĪÙĦ +Ġíķĺ ëĤĺ +ض ع +ê» ĺ +ÅĽ wi +ย ิ +ãģ¡ãĤĥ ãĤĵ +ĠÙħ Ø´ +à¸ĺ ี +ãģ¨ ãģį +׳×Ļ ×ķת +Ġë ¯ +Ġë¯ ¸ +Ġs ı +ëĭĪ ê¹Į +Ġп л +غ ÙĦ +à¹ģ รà¸ĩ +ب ÙĬر +ãģĤãĤĬ ãģ¾ãģĽãĤĵ +ê· ¼ +Ġy üz +ĠdeÄŁ er +åł´ åIJĪ +á» ¡ +м аÑĤ +รา à¸Ĭ +ÙĪر ÙĬ +ж ен +ãģ¾ ãĤĬ +ãģ® ä¸Ń +×Ļ×ĵ ×¢ +à¸Ń ุ +à¸ļ à¸Ńล +à¸Ľà¸±à¸į หา +ز Ùħ +ÄŁ a +à¸Ń ืà¹Ī +à¸Ńืà¹Ī à¸Ļ +п л +Ġне обÑħодим +׼ ×ij +à¹Ģ ศ +קר ×Ķ +ì² ĺ +ëł ¨ +×ŀק ×ķ×Ŀ +jÄħ c +Ùĩ ÙĦ +Ġ×¢ ×ij×ķ×ĵ +à¹Ħม à¹ī +à¸ģล ัà¸ļ +×ķ׼ ׾ +ק ×ĵ +اÙĦ ÙĬØ© +ر Ùĩ +ãģij ãĤĮãģ° +ĠÙĨ Ùģس +ãĤ¢ ãĥ« +ìĹ Īëĭ¤ +ק ×ķר +н еÑĢ +ب اب +ãĤ ¶ +سب ب +ÙĦ ÙĬÙĦ +ص ÙĨ +ص در +ế m +à¸Ĭà¹Īว à¸ĩ +ØŃ ÙĨ +Ġ×ij ×Ĵ +×ŀ ×ķ×¢ +׾ ×Ĺ +大 ãģį +ت ب +н еÑĤ +×Ļ×ij ×Ķ +б л +ãĥĹ ãĥª +اص Ø© +ãģ¤ ãģij +×Ļ×ŀ ×ķש +ãģĮ ãģĤ +ëĭ ´ +ãģĭãĤĤ ãģĹ +ãģĭãĤĤãģĹ ãĤĮ +ãģ¡ ãĤī +×ij ×ĺ +Ġba ÄŁ +×Ļ×Ĺ ×¡ +×ij ×ķ×¢ +ล ี +פע ×Ļ׾ +им и +g ÅĤ +Ġим е +خد اÙħ +×IJ ×Ļר +Ġy apt +ãģ¨ ãģĦ +à¸ĩ à¹Īาย +׾×Ļ ×ķ +ØŃد Ø« +را ÙĤ +ĠÄIJ i +اد ر +ãģĵãģ¨ ãĤĤ +×ij ×Ļר +Ġв з +ض اÙģ +ת ×ķ׼ +ÑĢ ом +ر ات +à¹Ģà¸Ĺ à¹Īา +ãģĺ ãĤĥ +ãģĿ ãģĵ +اج تÙħاع +à¹īà¸Ń à¸Ļ +ÙĤ Ùħ +ë³ ¸ +Ä ŀ +ש ×Ļ×ķ +×ij ׳×Ļ +ìľĦ ìĽIJ +à¹ģ à¸Ī +×Ĺ ×ķר +دÙĬ ÙĨØ© +ت Ø· +ằ m +ò a +ย à¸Ńà¸Ķ +Ġëĭ ¹ +สุ à¸Ĥ +×ĵר ×ļ +د ÙĨ +س ÙĬÙĨ +ÙĪÙĤ Ùģ +ÑĨ Ñĭ +г оÑĤов +еж дÑĥ +à¸ŀ วà¸ģ +اÙĤ تص +اÙĤتص اد +cz ÄĻ +ni ÄĻ +ÑĢ еб +ØŃ ÙĪ +à¸Ĺ à¹Į +ãĤĪ ãģŃ +д ж +à¸ģล à¹Īาว +دÙĬ Ø« +ãĤ³ ãĥŁ +ÙĤ ÙĪÙħ +Ġت ØŃ +à¹Ģ à¸ķิ +اÙģ ظ +à¸Ī ุ +رÙĬ اض +×ŀש ×ļ +à¹Ĥ ย +еÑĢ е +ãģ¿ ãģŁãģĦ +ìĿ´ ëĿ¼ +ĠاÙĦÙħ ÙĪ +ĠÑģÑĤ о +à¹Ģรà¹ĩ ว +Ġд еÑĤ +ĠÑģ дел +à¹Ģà¸Ĭ ืà¹Īà¸Ń +פ ׳×Ļ +ÙĪض ÙĪع +×ij ס +à¹ģ à¸Ķ +ó c +ริ ม +ÑĢаР´ +ìĪ ł +ãĥ¼ãĤ º +ãģ« ãģĬ +и но +פ ×Ļ׾ +à¸Ĭั à¹Īà¸Ļ +×Ĺ×ĵ ש +à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ +׳ ×Ļס +غ رب +ãĤ¸ ãĥ£ +ส ัà¸ĩ +à¹Ģ à¸Ĺีà¹Ī +à¹Ģà¸Ĺีà¹Ī ยว +ëŁ ¼ +à¹ģ à¸Ł +ãĥ¼ãĤ · +ãĥ¼ãĤ· ãĥ§ãĥ³ +Ġвоз мож +جÙħ ÙĪع +×ijר ×Ļ×Ŀ +ãĥĪ ãĥ© +ĠкаÑĩ еÑģÑĤв +Ø· ÙĬ +ÑĤ Ñı +צ ×ķ×¢ +ÄŁ ını +ع ÙĦÙī +ا Ø° +ÙĪاÙĤ ع +Ùħ ÙĪا +ائ ÙĬÙĦ +к ол +á»ģ m +à¸ľà¸¥ ิà¸ķ +×Ļ׳ ×ĺר +س Ùĥ +ש ×Ļר +ศึà¸ģ ษา +à¸ļ ั +Ñĩ аÑģ +×ķפ ×Ķ +×Ļפ ×ķ׾ +ĠاÙĦس اب +رÙĬ ب +ĠاÙĦ بÙĬ +ãĤ¹ ãĥĨ +Ñĩ ен +à¹ģ à¸ľ +Ġ׳ ש +ز ÙĬد +ØŃ Ø§Ø¯ +ëį Ķ +رÙĪ ع +à¸Ĺุ à¸Ļ +ส มา +c zeÅĦ +×Ļ×ĵ ×Ķ +ãģ§ ãģĤ +Ġçoc uk +Ø® ب +à¸ļ าย +à¸Ľà¸£à¸° à¸Ĭา +×ŀש ׾ +ãģª ãģĭ +à¸ģ าย +ãĥģ ãĥ£ +аÑĢ и +ĠÑĩ а +à¸Ķ ำ +à¸Ĺั à¹Īว +Ñĥ Ñħ +Ġö z +Ġì¢ ĭ +ج رÙĬ +ائ ÙĤ +à¸ł ัย +Ø· ار +د ارة +Ä© nh +Ø« ÙĨ +zell ik +اÙĦ ت +Ġg eli +ãĥķãĤ © +ол од +رب ع +שת ×ŀש +à¸ļร ร +íĿ ¬ +Ġü rün +Ġê·¸ ëłĩ +ศาส à¸ķรà¹Į +ãģ ľ +×Ļ×ij ׾ +ĠпÑĢед ÑģÑĤав +سط ÙĬÙĨ +ãĤĴ 使 +Ġпом оÑī +×ķק ר +ãĥ¯ ãĥ¼ +Ġyö net +×Ļק ר +à¸Ĥ า +еÑĢи ал +ØŃ Ùģ +Ġ×Ļ צ +à¸Ĺ ิ +å£ ² +à¸Ļ à¸Ńà¸ģ +×ķ׼ ר +íĻ ľ +ủ y +ĠاÙĦÙĤ ر +×Ļ×ij ×ķת +ÅĽ ni +Ùħ شار +ượ t +ĠÙĦ دÙĬ +ÑĤ ел +ĠØ¥ ÙĦÙĬ +عÙĦ ÙĪÙħ +ìķ ĺ +в иÑĤ +à¸Ħ ะ +yr ı +ãģ¨ ãģ£ãģ¦ +à¹Ģ à¸ī +à¸ĸ าม +ÙĤ ار +عÙĦ اÙħ +ặ ng +Ùħ ÙĴ +×Ļ×ŀ ת +سب Ø© +ãĤ¯ ãĥ© +×ķס ×£ +ĠпÑĢ ин +ãģĦ ãĤį +س اس +عت بر +วิ à¸Ĺย +วิà¸Ĺย า +س Ùĥر +ãĤ· ãĥ§ +ãģ ģ +ัà¸ģ ษ +×ij ×ķ×Ķ +ห ย +ãģ¾ ãĤĮ +ĠоÑĢг аниз +каз ал +ĠÑģв Ñıз +uy ết +ĠпÑĢо из +Ġק ×ĺ +à¹ģà¸ģ à¹ī +п ÑĥÑģ +Ġê·¸ ê²ĥ +ëĬ IJ +л екÑģ +ãĥ¼ãĥ Ĺ +à¸ķ ำ +ת×Ĺ ×Ļ׾ +à¸Ńà¸ĩ à¸Ħà¹Į +Ạµ +׳ צ +Ø£ Ø´ +Ø´ Ùĩ +ย ะ +à¸ģ à¸İ +ĠاÙĦØ¥ سÙĦاÙħ +ед ÑĮ +ãģ² ãģ¨ +ëıĦ ë¡Ŀ +ãģ© ãģ® +Ñĥ в +еÑĩ ение +ĠاÙĦت ج +ãģ« è¡Į +Ġп озв +ãĤı ãĤĬ +ÙĦ اث +íķĺ ìĺĢ +Ġм аÑĢ +Ġkon uÅŁ +ãĥ¬ ãĤ¹ +ãĤĴ æĮģ +ĠоÑģ нов +×Ĺ ×ij +ÙĪج ÙĪد +פ ×ķף +в оÑĢ +Ġн ик +ãģĭ ãĤĭ +ÅŁtır ma +×Ļס ×ĺ +Ø£ ÙĦ +ห à¹Į +и она +лÑĮ н +Ġг оÑģ +ĠÐľÐ¾Ñģ к +ÑĢ об +×ķ×IJ ×Ļ +ãģĬãĤĬ ãģ¾ãģĻ +ãģ£ãģ ± +к л +à¸Ļ à¸Ķà¹Į +رÙĬ Ùģ +اس ب +ĠÑĢ еÑĪ +Ġд ол +ãģ¹ ãģį +×Ļ×ij ×ķר +м еÑī +Ġна ÑĪ +à¹ģ à¸Ľà¸¥ +ÑĢ иÑĤ +кÑĥ Ñģ +и ÑĢа +аÑĤ ÑĥÑĢ +ÙĪا صÙĦ +à¹Ģà¸ľ ย +à¸Ń ำ +à¹Ģà¸ģ ิà¸Ļ +غ Ùħ +ãģĻ ãģİ +lı kl +ÅĦ sk +ê² ¬ +×Ļ׼ ×Ķ +׊ש×ij +ÙĪر ÙĬØ© +Ġд ейÑģÑĤв +×Ĺ׾ ×ĺ +Ġ׾ ×ŀ×¢ +צ׾ ×Ļ×Ĺ +еÑĩ а +Ùģ اع +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ еб +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ е +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ е +ק ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ãģ¨æĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ ا +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ اÙĨ +Ġ׾×Ķ ת +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ од +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +ק ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ פ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +Ø° Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ Ø¨ +ан Ñģ +س ÙĪ +ĠпÑĢ е +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +ק ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ùģض ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ Øª +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ Ø§ +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ãģ¨æĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +ק ×ķ׾ +ÑĢ ез +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ ан +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ سط +Ġв оп +Ġвоп ÑĢоÑģ +Ùħ ÙĬÙĨ +à¸Ħ à¸ĩ +×Ļר ×Ļ×Ŀ +c ów +ê² © +Ġê·¸ 룰 +Ġì§ Ħ +Ġש ׾×Ķ +à¹Ģร ิà¹Īม +à¸Ĭ à¸Ńà¸ļ +д еÑĤ +ÑİÑī иÑħ +à¸ļ à¸Ńà¸ģ +æĢĿ ãģĦ +ع ÙĬد +ס ×ŀ +×Ĵ ×Ļ×¢ +צ ×ĵ +ب ات +ĠëĶ° ëĿ¼ +à¸Ī ัà¸ĩ +ãģłãģij ãģ§ +×¢ ×Ļר +ĠÑĩ ел +ĠÑĩел ов +ĠÑĩелов ек +ãĥĥ ãĥģ +à¹Ģà¸ģ ีà¹Īยว +à¸Ķ ิ +Ġפ ×¢ +×Ļ×ŀ ×Ļ +ë° ĺ +Ø® ار +×ij ×Ļת +×¢ ×Ļ×Ŀ +ü yor +ãĤģ ãģ¦ +к лад +Ġ à¸Īาà¸ģ +à¹Ģà¸Ħ ย +ส à¸Ńà¸ĩ +à¹ģ à¸Ħà¹Ī +ẫ u +หà¸Ļ ัà¸ĩ +ש׾ ×ķ×Ŀ +اÙĨ ÙĬØ© +åĩº ä¼ļ +åĩºä¼ļ ãģĦ +à¸ł าย +à¸ļา à¸Ĺ +à¸Ĭา ว +mu ÅŁ +Ġ׾ק ×ij׾ +ãĤ· ãĥ£ +ĠÄ° ÅŁ +×Ĵ×ĵ ×ķ׾ +ج عÙĦ +ë³ Ģ +ยิ à¹Īà¸ĩ +à¸Ļ าย +à¸Ļ ีà¹Ī +วิ à¸ĺี +ãĤī ãģªãģĦ +ëł Ī +Ġ문 ìłľ +Ġ à¸ģ +à¸Ĺำ à¸ĩาà¸Ļ +à¹Ģว à¹ĩà¸ļ +ÑĦ е +楽 ãģĹ +สำ à¸Ħ +สำà¸Ħ ัà¸į +ر Ùħ +ãģķãĤĮ ãģ¦ +Ġоб ла +ר×IJ ×Ļ +หม à¸Ķ +ÙĨ ÙĬØ© +ли н +Ġe ÄŁ +it im +ëł ¹ +ص اÙĦ +ÅĽ l +à¸ľ ิà¸Ķ +ãĥŀ ãĥ³ +åħ¥ ãĤĮ +à¹Ģà¸ķ à¸Ńรà¹Į +ار ÙĬ +ĠÐ ¦ +d ür +ส วย +ë¦ ½ +رÙĥ Ø© +Ġh ã +×Ļת ×Ķ +à¸Ĥ à¸Ļา +à¸Ĥà¸Ļา à¸Ķ +à¸Īำ à¸Ļ +à¸Īำà¸Ļ วà¸Ļ +ש ×ķק +Ġд ом +ì± ħ +ãģĭ ãģij +פ ×ķ׾ +à¸Ĭ าย +Ñģ моÑĤÑĢ +Ñģл Ñĥж +ש ×IJ׾ +кÑĢÑĭ ÑĤ +Ġìŀ ĺ +é«ĺ ãģĦ +ĠÑĢ Ñĥк +ÙĨ ص +д ав +Æ°á» ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪا ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪد +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +ĠÄ° l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ ز +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ׾ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ од +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ اء +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +ק ×ķת +ÙĪج د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° à¸Īำ +ов ание +ãģĦ ãģ¤ +ãĥĥãĤ¯ ãĤ¹ +åIJĪ ãĤı +åIJĪãĤı ãģĽ +×Ļ׳ ×ķ×Ļ +ạ y +Ø« ÙĤ +ĠпÑĢ об +ĠпÑĢоб лем +ÅŁ eh +ÅŁeh ir +ع ادة +اÙĨ ÙĪÙĨ +à¸ķัว à¹Ģà¸Ńà¸ĩ +ì¶ ķ +ı lan +б ан +ãĥ³ ãĥī +à¸Ī ี +Ġ×Ķש ׳×Ļ +п оÑĤ +×ķ׾ ×Ļ×Ŀ +ล ัà¸ļ +ĠÑį ÑĤи +×ijק ש +ë¹Ħ ìĬ¤ +à¸Ńยà¹Īาà¸ĩ à¹Ħร +×Ļ׾ ×Ļ +à¹ĥà¸Ĭ à¹Ī +ĠاÙĦ ÙĥÙĦ +ãĥļ ãĥ¼ãĤ¸ +ص Ø© +ÑĤи ÑĢ +ãĤĵ ãģ© +зÑĭ к +wy ż +Ùĩ ÙĬ +ĠÙħ ÙĦÙĬ +Ġвид е +ظ اÙħ +دا ÙĪÙĦ +×ŀ ת×Ļ +Ġs ık +à¹Ģà¸ķิ ม +ãĤ¢ ãĤ¤ +ка Ñħ +צ ×Ļ׾ +à¹Ģà¸Ĭ à¹Īà¸Ļ +м аг +маг аз +магаз ин +à¸Ľ ั +à¸Ľà¸± à¸Ī +Ġש ×Ļר×ķת +ีย ม +ãĥĸ ãĥ« +Ġد ÙĪÙĦ +קר ×Ļ×Ŀ +Ùĩ Ùı +ов о +Ġü ret +د ÙĪÙĨ +à¹ģà¸Ļ ว +à¹Ģà¸Ļ ืà¹īà¸Ń +ĠÑĦ оÑĤ +ãĥ ĺ +ãģ¤ ãģĭ +Ñı Ñģ +ĠíķĺëĤĺ ëĭĺ +ائ ع +Ġп лаÑĤ +ìĺ Ī +Ġdost ÄĻp +ÙĪج Ùĩ +Ġ×Ķ ×Ĺ×Ļ +׳ ×Ļק +д ей +í ĽĦ +ı y +بØŃ Ø± +à¹Ģส ริม +Ġ׾ ×Ĵ +Ø°Ùĩ ب +ج ÙĬÙĦ +رÙĥ ز +Ġë ħ +Ġëħ ¸ +פ×Ļ׾ ×ķ +ãģ¾ ãģļ +iri ÅŁ +ĠÙĥ ÙĬÙģ +Ġ×ij צ +Ġêµ IJ +ÑĢоÑģ Ñģ +ĠØ´ ÙĬ +Ġiç er +×Ĵ ×ķ×ij×Ķ +мен но +×¢ ×ij×Ļר +×ķ×ŀ ×Ķ +ãĤī ãģĹãģĦ +ãģ ¼ +Ñī ин +è²· ãģĦ +جÙħÙĪع Ø© +Ġdön em +Ġ×ij ×IJר +в еÑģÑĤ +×ķר ×ķת +س Ùģ +à¹ģà¸Ĺ à¸Ļ +Ġд окÑĥменÑĤ +Ġا ÙĬ +ج اÙĨ +צ×ķ×¢ ×Ļ +ĠоÑģ об +ĠاÙĦÙħ س +ÑĢаР± +à¸ł ู +à¸Ķ าว +л екÑĤ +ع ÙĤ +×ķ×ĵ ×ķת +Ġol u +Ġolu ÅŁtur +ãģ¾ ãģ¾ +ед ин +à¹Ģ à¸Ńà¸ģ +ãĤµ ãĤ¤ +ëĦ Ī +Ø· ÙĨÙĬ +Ø· ÙĤØ© +ĠÐł аз +ÙĦ Ùij +Ñĩ ем +Ġ׾ ×ĺ +สั à¹Īà¸ĩ +سر ائÙĬÙĦ +Ġפר ×ĺ×Ļ +д еÑģÑĮ +Ġ׳ ׼ +اÙĨ ب +ÙĬا Ø© +Ùħ بر +Ġk ı +à¸Ľ à¸ı +à¸Ľà¸ı ิ +à¸ļั à¸ķิ +׳ ת×Ļ +ìĨ ¡ +ر اب +à¹ĥ à¸ķ +à¹ĥà¸ķ à¹ī +×Ļ׳ ת +ÙĪ ÙĬر +Ġ×Ķ×ŀ 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+èIJ½ ãģ¡ +åĩº ãģ¦ +ล ัà¸ģษ +Ġ×Ĵ ×ij×ķ×Ķ +ãĥĻ ãĥ« +ê±° ëĤĺ +ë§ IJ +×Ļ׾ ×ĵ×Ļ×Ŀ +ĠëĦ Ī +×ŀר ×Ļ +ร ส +ãĥŃ ãĥ³ +и ло +ноÑģÑĤÑĮ Ñİ +×ĸר ×Ĺ +п он +Ġ×Ķש ׾ +ê²ł ìĬµëĭĪëĭ¤ +Ġki ÅŁ +ĠÐļ и +ว ร +د اع +ÅŁ im +ÙĨ Ùij +в аÑĤ +را Ùĥ +ب اÙĦ +ид е +Ġ×Ķ×ŀ ×Ĺ +ìĸ µ +تÙģ اع +Ø£ ت +ëĬ ĺ +ש ×Ļת +ست Ùħر +ĠÑĦ ак +ĠاÙĦØ£Ùħ رÙĬ +ëŀ ¨ +اس Ùħ +Ġa ÄŁ +Ġç ev +Ùĥ ÙĪر +ãģķ ãģ¾ +Ġç öz +Ġر س +Äħ da +สà¸Ļ ุ +ãģĹãģ¦ ãģıãĤĮ +н Ñİ +leÅŁ me +ãĤª ãĥ³ +ãģ¨ ãģªãĤĬ +ava ÅŁ +×ĺ ×Ļ×ij +ØŃ Ø¶ +×ķצ ×IJ×ķת +ÙĨ ÙħÙĪ +ı t +ĠÑħ а +ĠÑħа ÑĢак +ĠÑħаÑĢак ÑĤеÑĢ +Ġd ÅĤ +ãĥĹ ãĥ© +à¸Ĭ ุม +à¹Ī à¸Ńà¸Ļ +×ķ×ij ׾ +Ñģ ол +×ĵ ×Ĵ +аÑĢ аÑĤ +n ivers +Ġgerçek leÅŁtir +ĠاÙĦ ÙĦÙĬ +ระ ยะ +ĠÙħ ختÙĦÙģ +Ġgö nder +Ùģ ار +do ÄŁ +doÄŁ an +ص ÙĦاØŃ +Ġyay ın +ãĥĨ ãĥ³ +รว à¸Ī +×Ļ×Ĺ ×Ļ×ĵ +ünk ü +ÑĨи алÑĮн +à¸ļ ู +ม ุ +h ä +Ø® Ùģ +å¢ Ĺ +å¢Ĺ ãģĪ +еÑĩ но +ĠاÙĦس ÙĨ +à¸Ĥ าว +im di +Ð « +à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ +à¸ļา ล +ת ש +Ġdüzen le +мÑĭ Ñģл +ãģı ãģª +ż u +Ġwsp óÅĤ +Ġн аз +ınd aki +تر Ø© +ÅŁ ek +Ġö d +ĠÙĪ Ùĥ +Ġпозв олÑı +Ġת ×ķ׼ +ÙħÙĨ تج +ë§ ī +ĠاÙĦØ« ÙĦاث +аÑĨи Ñİ +ÙĪر ÙĪ +Ñĭв 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ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh Æ°ng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +ĠØ° ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt Æ° +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ק +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪاÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch ủ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij Æ°á»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth Æ°á»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ Øª +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ ت +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth Æ°Æ¡ng +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ ت +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪر +Ġtr ÃŃ +ĠÐŁÑĢ о +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch Æ°a +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph Æ°Æ¡ng +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ о +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth ủ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ а +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд 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+ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +ק ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ Ø¯Ø© +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij ủ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġק ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ׼×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth Æ° +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ פר +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦØ° ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ Ø° +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk ý +Ġch ân +Ġ à¹Ħมà¹Ī +اØŃ Ø© +r án +׳×Ļ ×Ļ×Ŀ +Ġ×ij ף +ĠÐ ĸ +à¸ķร à¸ĩ +д Ñĭ +Ġs ắc +ÙĦ ت +ãĥŃ ãĥ¼ +ĠÙĦ ÙĨ +Ġר ×ķ +Ġd Æ°á»Ľi +à¹Ģ à¸ĺ +à¹Ģà¸ĺ à¸Ń +e ÄŁi +Ġ×ķ ש +ĠÙĦ Ø£ +Ġg ặp +Ġc á»ij +ãģ¨ ãģ¦ãĤĤ +رÙĪ س +Ġ׾×Ķ ×Ļ +Ġë³ ¸ +ä¸Ĭ ãģĴ +Ġm ức +Ñħ а +Ġìŀ ¬ +à¸ī ัà¸Ļ +ÑĢÑĥ ж +Ġaç ık +ÙĪ اÙĦ +Ġ×ĸ ×ŀף +人 ãģ¯ +ع ÙĬÙĨ +Ñı Ñħ +Ġ×Ĵ×ĵ ×ķ׾ +ר ×ķ×ij +g ó +ëĿ¼ ê³ł +Ġark adaÅŁ +ÙĨ شر +Ġгод Ñĥ +ĠболÑĮ ÑĪе +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġcâ u 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Ñĭе +ëĤ ł +Ġв ам +بÙĦ غ +Ġê°Ļ ìĿĢ +íĮ IJ +ÙĦ ب +Ġnas ıl +Ġод ин +м ан +ĠعÙĦÙĬ Ùĩا +б и +Ġפ ש×ķ×ĺ +×ijר ×Ļ +Ġש ׳×Ķ +Ġëı Ħ +ĠÄIJ ại +Ġ×IJ×ķת ×Ŀ +ĠاÙĦØŃ Ø± +Ġб о +à¸Ī ุà¸Ķ +Ġr õ +ĠdeÄŁi ÅŁ +Ġëĭ ¨ +ĠÑģлÑĥÑĩ а +ĠÑģлÑĥÑĩа е +Ġ×IJ׳ ש×Ļ×Ŀ +×ĵ ×£ +ש×ij ת +Ġש׾ ׼×Ŀ +Ġch ú +nik ów +Ġtan ı +Ġcá o +ĠÄij á +Ġ×IJ ×ĵ×Ŀ +Ġê° ķ +Ġnhi á»ĩm +Ġ׾ ס +Ġ×Ľ×ª ×ij +Ġ×Ķס פר +ĠÄij Äĥng +Ġë ijIJ +à¸ľ ิ +à¸ľà¸´ ว +ج ا +Ġê° IJ +ر Ø£ +ست خدÙħ +ãģ«ãģªãĤĬ ãģ¾ãģĻ +Ġtá» · +×ĺ ×ķר +г овоÑĢ +Ġв оÑģ +ĠÙħÙĨ Ùĩا +иÑĢов аÑĤÑĮ +ĠÄij ầy +׳ ×Ĵ +ĠÙħ ÙĪ +ĠÙħ ÙĪÙĤع +ר׼ ×Ļ +ت Ùı +ëª ¨ +Ġת ×ķ +ÙĬا Ùĭ +à¹ĥ à¸Ķ +ãĤĬ ãģ¾ãģĻ +à¸Ńยูà¹Ī à¹ĥà¸Ļ +ĠØ£ ÙĪÙĦ +ĠØ£ خرÙī +Ġc Æ° +ص ار +×ŀ׊ש×ij +б ÑĢа +ÅĦ ski +б ÑĢ +ĠÙĬ Ùı +à¸ģ ิà¸Ļ +Ġch á»ijng +Ùħ Ùı +Ġ à¸Ħืà¸Ń +Ġت ÙĨ +t ÃŃ +y Äĩ +Ġm ạng +Ùģ ÙĪ +Ġdü nya +ק ר×IJ +Ġק ׾ +ĠØŃ Ø§ÙĦ +c ÃŃa +Ġà¹Ģ รา +Ġר ×ķצ×Ķ +Ġá p +ë° ķ +ا ÙĤØ© +ни Ñİ +Ġ×IJ ׾×ķ +Ġ×ŀס ×ķ +ãģ§ãģ¯ ãģªãģı +Ġtr ả +Ġק שר +mi ÅŁtir +Ġl Æ°u +Ġh á»Ĺ +ĠбÑĭ ли +Ġl ấy +عÙĦ Ùħ +Ġö zel +æ°Ĺ ãģĮ +Ġ×ĵ ר×ļ +Ùħ د +s ını +׳ ×ķש×IJ +r ów +Ñĩ еÑĢ +êµIJ ìľ¡ +ĠÐľ о +л ег +ĠV Ỽi +วัà¸Ļ à¸Ļีà¹ī +ÑİÑī ие +ãģĬ ãģĻ +ãģĬãģĻ ãģĻ +ãģĬãģĻãģĻ ãĤģ +ëı ħ +Ġ×Ļ×Ķ ×Ļ×Ķ +×ŀ ×ĺר +Ñı ми +Ġl á»±a +ĠÄij ấu +à¹Ģส ียà¸ĩ +Ġt Æ°Æ¡ng +ëĵ ± +ĠÑģÑĤ аÑĢ +à¹ĥ à¸ļ +ว ัà¸Ķ +ĠÄ° stanbul +Ġ à¸Īะ +à¸ķ ลาà¸Ķ +Ġب ÙĬ +à¹ģà¸Ļ ะ +à¹ģà¸Ļะ à¸Ļำ +س اعد +Ġب Ø£ +Ġki á»ĥm +ØŃ Ø³Ø¨ +à¸Ĭั à¹īà¸Ļ +Ġ×ķ ×¢×ķ×ĵ +ов ÑĭÑħ +оÑģ нов +Ġtr Æ°á»Łng +צ ×ij×¢ +ĠÃŃ t +Ġk ỹ +cr é +Ñı м +êµ ° +ãģĮ ãģªãģĦ +ÙĬÙĦ Ø© +ãĥķ ãĤ£ +ر Ùī +ĠÙĬ جب +Ġ×IJ ×£ +Ġc á»±c +ãĤīãĤĮ ãģŁ +Ġ à¸ľà¸¹à¹ī +Ġ à¸Ń +lar ımız +Ġkad ın +Ġê·¸ ëŀĺ +Ġê·¸ëŀĺ ìĦľ +ĠëĺIJ ëĬĶ +ĠÄij ả +ĠÄijả m +Ġ×IJ ×ķ×ŀר +Ġy ếu +ci Äħ +ciÄħ g +Ġt á»ij +Ġש×IJ ׳×Ļ +Ġdz iaÅĤa +Ñī а +ĠÄij Ãłn +s ına +ãģĵãĤĮ ãģ¯ +Ġ×ij ׾×Ļ +Ġ×ij ×Ļשר×IJ׾ +л оÑģÑĮ +Ġgi ữ +ê° IJ +ÑĢ он +تج ار +г лав +в ин +Ġh ạn +Ġyapı lan +ب س +Ġ à¸ŀรà¹īà¸Ńม +ê´Ģ 리 +mÄ±ÅŁ tır +b ü +r ück +ĠBaÅŁkan ı +ĠÙĦ ÙĬس +Ġs Æ¡ +à¸Īัà¸ĩ หว +à¸Īัà¸ĩหว ัà¸Ķ +د اء +Ġ×Ķ ׼ +v ÃŃ +ש ×IJר +Ġh Æ°á»Łng +Ġb óng +ĠCh ÃŃnh +Äħ c +à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ +Ġtá» © +Ġtứ c +ĠÑĨ веÑĤ +Ġt á»iji +ĠnghÄ© a +ÙĦا عب +د ÙĦ +Ġפע ×Ŀ +h ör +à¸Ĭ 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×ķ×Ļ×ķת +ei ÃŁ +ãĥ ¨ +ìĥ Ī +ĠÃĩ a +Æ ¯ +ש ×Ĵ +ÙĬÙĨ Ø© +ร à¹īà¸Ńà¸ĩ +ãĤµ ãĥ³ +ÑĢоÑģÑģ ий +ÑĢоÑģÑģий Ñģк +a ÄŁa +ĠнаÑĩ ина +Ġص ÙĦÙī +à¸Ĺุà¸ģ à¸Ħà¸Ļ +íļĮ ìĤ¬ +Ġли ÑĨ +Ø´ ÙĬر +ĠØ´ÙĬ Ø¡ +ÙĬÙĨ ا +Ġפ ×Ĺ×ķת +Ġiçer is +Ġiçeris inde +ĠØ£ ØŃÙħد +Ġże by +ì´ Ŀ +Ġп оказ +Ġи менно +หà¸Ļัà¸ĩ ส +หà¸Ļัà¸ĩส ืà¸Ń +ĠÑĤÑĢ е +สัà¸ĩ à¸Ħม +Ø¥ ÙIJ +ãģĮ å¿ħè¦ģ +ÙĬÙij Ø© +פ צ +íĭ ° +ĠÙħ جاÙĦ +׳ פש +к ан +×Ĺ ×ķפ +×Ĺ×ķפ ש +ì²ĺ ëŁ¼ +ов аÑı +з ов +Ġh ạ +Ġdzi ÄĻki +×Ļר ×ķ +Ġ׾ ×ŀצ +Ġ׾×ŀצ ×ķ×IJ +×Ļ×ĵ ×ķ +Ġs ợ +Ġ׾×Ķ ×Ĵ×Ļ×¢ +ק ×ij×¢ +Ġchi á»ģu +ãĥŀ ãĤ¤ +Ġd Ãłng +à¹ģà¸Ł à¸Ļ +Ġü ye +×Ļ׳ ×Ĵ +à¹Ģรีย à¸ģ +ç§ģ ãģĮ +th é +ĠÑĦ илÑĮ +ĠÑĦилÑĮ м +ĠNg Ãły +Ġж ен +Ġжен Ñīин +ج ÙĬد +n ç +à¸Ľ รา +×Ļ×ŀ ×ķ +Ġn á»ģn +×IJ ×ķ׾×Ŀ +Ġвозмож ноÑģÑĤÑĮ +Ġëĭ¤ ìĭľ +è¦ĭ ãģŁ +à¸ĸ à¸Ļ +à¸ĸà¸Ļ à¸Ļ +mız ı +ĠÙħ جÙħÙĪعة +c jÄħ +ĠÐł Ф +à¸ģำ หà¸Ļ +à¸ģำหà¸Ļ à¸Ķ +ĠìŬ 기 +land ı +ни ÑĨ +ÑģÑĤв е +Ġ×ĵ ×ijר×Ļ×Ŀ +Ġsk ÅĤad +ãĤĬ ãģ¾ãģĹãģŁ +ĠоÑĤ кÑĢÑĭÑĤ +нÑı ÑĤ +ĠÑģво ей +à¸Ī ิà¸ķ +ĠкаÑĩеÑģÑĤв е +Ġet tiÄŁi +ìĤ¬ íķŃ +ĠاÙĦÙĬ ÙħÙĨ +иÑĩеÑģки й +ë¸ Į +Ġ×ij×IJר ×¥ +Ġا سÙħ 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+تط ÙĪÙĬر +Ġsay ı +ĠÑģ ейÑĩаÑģ +Ġ×IJ×Ĺר ת +ק ×ķפ×Ķ +ק×ķר ס +Ġس Ùħ +Ġ×ĺ ×Ļפ×ķ׾ +ìĿ´ëĿ¼ ëĬĶ +دراس Ø© +èµ· ãģĵ +×Ĺ ×Ļ׳ +×Ĺ×Ļ׳ ×ķ×ļ +×ĵ ק +Ġë§ ŀ +Ġком анд +ĠÐij о +Ġиг ÑĢÑĭ +à¸ļ ี +ĠØ£ Ùİ +в ен +ĠاÙĦج دÙĬد +ĠÙĦ Ø¥ +Ġ×ķ×IJ ׳×Ļ +Ġ×Ķס ×Ļ +иÑĩеÑģк ого +رÙĪ ØŃ +à¸ģาร ศึà¸ģษา +ĠTr Æ°á»Ŀng +иг ÑĢа +ıl ması +Ġм аÑģÑģ +ãģ¨ãģį ãģ« +à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ +à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา +ĠاÙĦساب ÙĤ +Ġ×ŀ×¢ ×ĺ +в аÑĤÑĮ +m Ã¼ÅŁ +Ġ׾ ׼×ļ +Ġt á»ĭch +Ùģ ÙĩÙħ +تد رÙĬب +Ø´ Ùĥ +Ġ×ij ×ŀ×Ļ +Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ +ÙĤØ· اع +ãģª ãģĹ +×ķצ ×Ļ×IJ +ĠÙĪ سÙĬ +з Ñĥ +Ġy at +Ġyat ırım +ë§ İ +Ġth ắng +ãģĬ 客 +ãģĬ客 æ§ĺ +ĠThi ên +ãģ«å¯¾ ãģĹãģ¦ +ÑĢ иÑģ +ÙĨت ائ +ÙĨتائ ج +Ġ×ŀ שר +Ġ×ŀשר ×ĵ +Ġتع اÙĦ +ĠتعاÙĦ Ùī +ש ׳×Ļ +Ùĩ اÙħ +×IJ׳ ש×Ļ×Ŀ +Ġżyc ia +ĠÑĢÑĥб лей +ÙĬ ض +Ġkat ıl +ĠÙħ ÙĪضÙĪع +Ġvard ır +ĠÙħÙĨ Ø·ÙĤØ© +ĠTr ần +Ġв еÑģ +ü p +Ùħ ÙĪÙĨ +ÑĪ ли +Ġn óng +Ø® ÙĦÙģ +ĠС ÑĤа +Ġд оÑĢ +ĠдоÑĢ ог +ĠwÅĤa ÅĽnie +eÄŁ in +Ġhi á»ĥm +ĠС ам +ê»ĺ ìĦľ +ĠÑĦ а +ãģ» ãģĨ +ãģ»ãģĨ ãģĮ +×ķפ ×Ļ×¢ +ê° Ī +د ÙĪÙĦ +Ġthu ê +Ġch á»Ĺ +Ġëĭ¹ ìĭł +ãģij ãĤĮ +ãģijãĤĮ ãģ© +ë³´ íĺ¸ +ãģķãĤĮ 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--git a/training_logs/20260612_220124_metrics_job_630127.csv b/training_logs/20260612_220124_metrics_job_630127.csv new file mode 100644 index 0000000..47e57a6 --- /dev/null +++ b/training_logs/20260612_220124_metrics_job_630127.csv @@ -0,0 +1,29 @@ 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b/training_logs/20260612_220124_metrics_job_630130.csv new file mode 100644 index 0000000..b07a06b --- /dev/null +++ b/training_logs/20260612_220124_metrics_job_630130.csv @@ -0,0 +1,15 @@ 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+async/discard_rate,async/discarded_count,async/effective_batch_groups,async/effective_batch_samples,async/staleness_max,async/staleness_mean,async/staleness_min,async/staleness_ratio,generate/avg_num_tokens,generate/avg_tokens_non_zero_rewards,generate/avg_tokens_zero_rewards,generate/max_num_tokens,generate/min_num_tokens,generate/std_num_tokens,loss/avg_final_rewards,loss/avg_raw_advantages,loss/avg_raw_advantages_abs,policy/final_loss,policy/log_ratio_abs_max,policy/log_ratio_abs_mean,policy/log_ratio_abs_p99,policy/log_ratio_abs_pos00,policy/log_ratio_abs_pos10,policy/log_ratio_abs_pos20,policy/log_ratio_abs_pos30,policy/log_ratio_abs_pos40,policy/log_ratio_abs_pos50,policy/log_ratio_abs_pos60,policy/log_ratio_abs_pos70,policy/log_ratio_abs_pos80,policy/log_ratio_abs_pos90,policy/n_tokens_dp_gt_10pct,policy/n_tokens_dp_gt_1pct,policy/n_tokens_dp_gt_50pct,policy/policy_entropy,policy/policy_loss,policy/policy_lr,policy/policy_update_steps,policy/ppo_clip_ratio,policy/raw_grad_norm,reward/avg_pass_at_8,reward/avg_raw_reward,system/process_rss_gb,system/process_vms_gb,system/ram_available_gb,system/ram_percent,system/ram_total_gb,system/ram_used_gb,timing/compute_advantages_and_returns,timing/convert_to_training_input,timing/fwd_logprobs_values_reward,timing/policy_train,timing/run_training,timing/step,timing/sync_weights,timing/train_critic_and_policy,timing/wait_for_generation_buffer,trainer/epoch,trainer/global_step,batch_errors/total_batches,batch_errors/total_instances,batch_errors/total_successful,batch_errors/total_failed,batch_errors/total_masked,batch_errors/avg_InvalidChatHistory,batch_errors/total_InvalidChatHistory,batch_errors/avg_ContextLengthExceededError,batch_errors/total_ContextLengthExceededError,batch_errors/avg_VerifierTimeoutError,batch_errors/total_VerifierTimeoutError,timing/cleanup_old_checkpoints,timing/save_checkpoints,batch_errors/avg_AgentTimeoutError,batch_errors/total_AgentTimeoutError,batch_errors/avg_DaytonaError,batch_errors/total_DaytonaError,timing/save_hf_model,batch_errors/avg_AgentSetupTimeoutError,batch_errors/total_AgentSetupTimeoutError 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a/training_logs/20260612_220124_metrics_report.md b/training_logs/20260612_220124_metrics_report.md new file mode 100644 index 0000000..cb6cbe7 --- /dev/null +++ b/training_logs/20260612_220124_metrics_report.md @@ -0,0 +1,393 @@ +# SkyRL Training Metrics Analysis + +Generated from 4 log files + +## Overview + +| Log File | Total Steps | Metric Blocks | Final Reward (mean) | Final Reward (max) | Total Time (s) | +|----------|-------------|---------------|---------------------|-------------------|----------------| +| job_630124 | 28 | 28 | 0.4467 | 0.7695 | 41354.5 | +| job_630127 | 56 | 28 | 0.4329 | 0.5703 | 40185.7 | +| job_630130 | 70 | 14 | 0.3858 | 0.5449 | 36451.3 | +| job_661735 | 78 | 8 | 0.3747 | 0.5566 | 13910.7 | + +## Async Metrics + +| | Mean | Std | Min | Max | Count | +|:------------------------------|-----------:|---------:|------:|------:|--------:| +| async/discard_rate | 0 | 0 | 0 | 0 | 78 | +| async/discarded_count | 0 | 0 | 0 | 0 | 78 | +| async/effective_batch_groups | 64 | 0 | 64 | 64 | 78 | +| async/effective_batch_samples | 512 | 0 | 512 | 512 | 78 | +| async/staleness_max | 3.52564 | 1.00291 | 0 | 7 | 78 | +| async/staleness_mean | 1.76964 | 0.525328 | 0 | 4 | 78 | +| async/staleness_min | 0.153846 | 0.625786 | 0 | 4 | 78 | +| async/staleness_ratio | 0.872991 | 0.178479 | 0 | 1 | 78 | + +## Generate Metrics + +| | Mean | Std | Min | Max | Count | +|:-------------------------------------|-----------:|---------:|---------:|---------:|--------:| +| generate/avg_num_tokens | 4953.5 | 781.064 | 1687.9 | 6119.26 | 78 | +| generate/avg_tokens_non_zero_rewards | 4928.65 | 524.786 | 3823.39 | 6195.01 | 78 | +| generate/avg_tokens_zero_rewards | 5030.87 | 1001.93 | 1110.32 | 6614.23 | 78 | +| generate/max_num_tokens | 31246.8 | 2087.2 | 19335 | 31885 | 78 | +| generate/min_num_tokens | 17.1795 | 142.893 | 1 | 1263 | 78 | +| generate/std_num_tokens | 4459.29 | 622.221 | 2744.51 | 5829.72 | 78 | + +## Loss Metrics + +| | Mean | Std | Min | Max | Count | +|:----------------------------|----------:|----------:|--------:|-------:|--------:| +| loss/avg_final_rewards | 0.423427 | 0.0901758 | 0.2051 | 0.7695 | 78 | +| loss/avg_raw_advantages | 0.0121513 | 0.0324204 | -0.0162 | 0.1959 | 78 | +| loss/avg_raw_advantages_abs | 0.106845 | 0.0320619 | 0.0431 | 0.2355 | 78 | + +## Policy Metrics + +| | Mean | Std | Min | Max | Count | +|:----------------------------|-------------:|------------:|--------:|-------:|--------:| +| policy/final_loss | -9.23077e-05 | 0.000277638 | -0.0015 | 0 | 78 | +| policy/log_ratio_abs_max | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_mean | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_p99 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos00 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos10 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos20 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos30 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos40 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos50 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos60 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos70 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos80 | 0 | 0 | 0 | 0 | 78 | +| policy/log_ratio_abs_pos90 | 0 | 0 | 0 | 0 | 78 | +| policy/n_tokens_dp_gt_10pct | 0 | 0 | 0 | 0 | 78 | +| policy/n_tokens_dp_gt_1pct | 0 | 0 | 0 | 0 | 78 | +| policy/n_tokens_dp_gt_50pct | 0 | 0 | 0 | 0 | 78 | +| policy/policy_entropy | 0.119653 | 0.0179549 | 0.0511 | 0.1411 | 78 | +| policy/policy_loss | -0.00595641 | 0.017477 | -0.0959 | 0 | 78 | +| policy/policy_lr | 0 | 0 | 0 | 0 | 78 | +| policy/policy_update_steps | 1 | 0 | 1 | 1 | 78 | +| policy/ppo_clip_ratio | 0 | 0 | 0 | 0 | 78 | +| policy/raw_grad_norm | 0.0149538 | 0.00982649 | 0.0068 | 0.0967 | 78 | + +## Reward Metrics + +| | Mean | Std | Min | Max | Count | +|:----------------------|---------:|----------:|-------:|-------:|--------:| +| reward/avg_pass_at_8 | 0.543153 | 0.079973 | 0.375 | 0.8125 | 78 | +| reward/avg_raw_reward | 0.423427 | 0.0901758 | 0.2051 | 0.7695 | 78 | + +## System Metrics + +| | Mean | Std | Min | Max | Count | +|:------------------------|---------:|-------------:|---------:|---------:|--------:| +| system/process_rss_gb | 14.2314 | 1.13198 | 9.9914 | 16.2064 | 78 | +| system/process_vms_gb | 51.7172 | 0.827814 | 50.291 | 53.4671 | 78 | +| system/ram_available_gb | 570.794 | 11.4823 | 551.846 | 594.612 | 78 | +| system/ram_percent | 33.4705 | 1.34049 | 30.7 | 35.7 | 78 | +| system/ram_total_gb | 857.968 | 3.05352e-05 | 857.967 | 857.968 | 78 | +| system/ram_used_gb | 287.174 | 11.4823 | 263.356 | 306.122 | 78 | + +## Timing Metrics + +| | Mean | Std | Min | Max | Count | +|:--------------------------------------|------------:|------------:|---------:|-----------:|--------:| +| timing/compute_advantages_and_returns | 0.102533 | 0.107328 | 0.0486 | 0.8661 | 78 | +| timing/convert_to_training_input | 4.70007 | 0.352461 | 2.7178 | 5.493 | 78 | +| timing/fwd_logprobs_values_reward | 35.7722 | 9.32356 | 15.1498 | 104.946 | 78 | +| timing/policy_train | 207.902 | 31.034 | 108.532 | 269.709 | 78 | +| timing/run_training | 244.034 | 34.7885 | 123.918 | 312.363 | 78 | +| timing/step | 1691.05 | 1167.37 | 558.623 | 10105.4 | 78 | +| timing/sync_weights | 22.7971 | 1.88627 | 17.1266 | 26.2516 | 78 | +| timing/train_critic_and_policy | 208.159 | 31.0529 | 108.708 | 270.02 | 78 | +| timing/wait_for_generation_buffer | 1419.52 | 1176.94 | 247.59 | 9825.37 | 78 | +| timing/cleanup_old_checkpoints | 4.0377 | 18.4601 | 0.0067 | 113.745 | 39 | +| timing/save_checkpoints | 14.6624 | 18.3571 | 7.9437 | 123.126 | 39 | +| timing/save_hf_model | 24.8864 | 69.3195 | 5.8995 | 275.429 | 15 | + +## Trainer Metrics + +| | Mean | Std | Min | Max | Count | +|:--------------------|-------:|--------:|------:|------:|--------:| +| trainer/epoch | 0 | 0 | 0 | 0 | 78 | +| trainer/global_step | 39.5 | 22.6605 | 1 | 78 | 78 | + +## Batch_Errors Metrics + +| | Mean | Std | Min | Max | Count | +|:----------------------------------------------|------------:|-----------:|-------------:|-----------:|--------:| +| batch_errors/total_batches | 61.5256 | 7.45676 | 44 | 119 | 78 | +| batch_errors/total_instances | 492.205 | 59.6541 | 352 | 952 | 78 | +| batch_errors/total_successful | 432.179 | 76.3362 | 191 | 918 | 78 | +| batch_errors/total_failed | 20.7308 | 9.45897 | 8 | 58 | 78 | +| batch_errors/total_masked | 49.3333 | 27.27 | 6 | 160 | 78 | +| batch_errors/avg_RuntimeError | 0.10335 | 0.0403443 | 0.0420168 | 0.15873 | 6 | +| batch_errors/total_RuntimeError | 6.83333 | 1.94079 | 5 | 10 | 6 | +| batch_errors/avg_VerifierTimeoutError | 0.200918 | 0.399779 | 0.015625 | 1.94915 | 61 | +| batch_errors/total_VerifierTimeoutError | 12.1639 | 24.1283 | 1 | 115 | 61 | +| batch_errors/avg_AgentTimeoutError | 0.184673 | 0.486823 | 0.00840336 | 2.55932 | 62 | +| batch_errors/total_AgentTimeoutError | 11.1613 | 29.1948 | 1 | 151 | 62 | +| batch_errors/avg_InvalidChatHistory | 0.549849 | 0.239037 | 0.10084 | 1.41379 | 78 | +| batch_errors/total_InvalidChatHistory | 33.5256 | 14.2343 | 7 | 82 | 78 | +| batch_errors/avg_ContextLengthExceededError | 0.161599 | 0.087392 | 0.015873 | 0.460317 | 77 | +| batch_errors/total_ContextLengthExceededError | 9.84416 | 5.14287 | 1 | 29 | 77 | +| batch_errors/avg_DaytonaError | 0.0555526 | 0.0370314 | 0.016129 | 0.114754 | 10 | +| batch_errors/total_DaytonaError | 3.4 | 2.27058 | 1 | 7 | 10 | +| batch_errors/avg_AgentSetupTimeoutError | 0.0930756 | 0.121065 | 0.0153846 | 0.372881 | 18 | +| batch_errors/total_AgentSetupTimeoutError | 5.5 | 7.15583 | 1 | 22 | 18 | +| batch_errors/avg_DaytonaAuthenticationError | 0.0222833 | 0.0083652 | 0.015873 | 0.031746 | 3 | +| batch_errors/total_DaytonaAuthenticationError | 1.33333 | 0.57735 | 1 | 2 | 3 | + +## Training Progression by Log + +### job_630124 + +| Step | Reward | Pass@8 | KL | Loss | Step Time (s) | Gen Wait (s) | +|------|--------|--------|-----|------|---------------|-------------| +| 1 | 0.7695 | 0.8125 | 0.000000 | 0.0000 | 3372.0 | 3167.0 | +| 2 | 0.4199 | 0.5781 | 0.000000 | -0.0001 | 1718.0 | 1388.3 | +| 3 | 0.3457 | 0.4688 | 0.000000 | -0.0000 | 1220.7 | 885.5 | +| 4 | 0.4141 | 0.4844 | 0.000000 | -0.0001 | 1357.9 | 1059.7 | +| 5 | 0.5156 | 0.6250 | 0.000000 | -0.0001 | 1224.4 | 955.5 | +| 6 | 0.4023 | 0.4844 | 0.000000 | -0.0000 | 1281.0 | 982.2 | +| 7 | 0.4492 | 0.5469 | 0.000000 | -0.0000 | 1559.3 | 1299.2 | +| 8 | 0.4766 | 0.6250 | 0.000000 | 0.0000 | 1610.3 | 1270.4 | +| 9 | 0.4453 | 0.5469 | 0.000000 | 0.0000 | 1321.3 | 1032.5 | +| 10 | 0.3730 | 0.5312 | 0.000000 | 0.0000 | 1294.9 | 1011.8 | +| 11 | 0.4922 | 0.6562 | 0.000000 | -0.0001 | 1475.0 | 1214.5 | +| 12 | 0.3477 | 0.4219 | 0.000000 | 0.0000 | 1559.4 | 1264.5 | +| 13 | 0.5020 | 0.6250 | 0.000000 | -0.0000 | 1431.9 | 1188.5 | +| 14 | 0.4355 | 0.5469 | 0.000000 | -0.0000 | 1142.8 | 885.9 | +| 15 | 0.4902 | 0.6094 | 0.000000 | -0.0000 | 1314.0 | 1035.8 | +| 16 | 0.4512 | 0.5781 | 0.000000 | -0.0000 | 1308.8 | 1024.4 | +| 17 | 0.3887 | 0.5156 | 0.000000 | -0.0000 | 1220.9 | 899.1 | +| 18 | 0.4551 | 0.5625 | 0.000000 | -0.0000 | 1582.7 | 1299.8 | +| 19 | 0.3691 | 0.4531 | 0.000000 | -0.0000 | 1533.3 | 1254.4 | +| 20 | 0.4023 | 0.5156 | 0.000000 | 0.0000 | 1501.3 | 1228.6 | +| 21 | 0.3633 | 0.4688 | 0.000000 | -0.0001 | 1384.7 | 1125.9 | +| 22 | 0.4473 | 0.5781 | 0.000000 | 0.0000 | 1341.1 | 1063.0 | +| 23 | 0.4160 | 0.5781 | 0.000000 | -0.0000 | 1636.8 | 1328.4 | +| 24 | 0.3535 | 0.4531 | 0.000000 | 0.0000 | 1813.1 | 1525.8 | +| 25 | 0.4121 | 0.4844 | 0.000000 | -0.0000 | 988.4 | 747.1 | +| 26 | 0.5898 | 0.6875 | 0.000000 | -0.0001 | 1031.5 | 783.3 | +| 27 | 0.4199 | 0.5469 | 0.000000 | -0.0002 | 2131.0 | 1853.8 | +| 28 | 0.5605 | 0.6250 | 0.000000 | -0.0003 | 998.4 | 778.3 | + +### job_630127 + +| Step | Reward | Pass@8 | KL | Loss | Step Time (s) | Gen Wait (s) | +|------|--------|--------|-----|------|---------------|-------------| +| 29 | 0.5430 | 0.6610 | 0.000000 | -0.0001 | 3907.1 | 3661.5 | +| 30 | 0.3984 | 0.5156 | 0.000000 | -0.0000 | 1561.1 | 1238.2 | +| 31 | 0.2578 | 0.3906 | 0.000000 | -0.0001 | 558.6 | 247.6 | +| 32 | 0.4316 | 0.5469 | 0.000000 | -0.0001 | 1881.7 | 1636.3 | +| 33 | 0.3945 | 0.5469 | 0.000000 | 0.0000 | 1516.1 | 1212.1 | +| 34 | 0.4160 | 0.5156 | 0.000000 | -0.0000 | 1068.5 | 739.5 | +| 35 | 0.4102 | 0.5000 | 0.000000 | -0.0000 | 1384.1 | 1112.5 | +| 36 | 0.4609 | 0.6406 | 0.000000 | -0.0000 | 1464.7 | 1177.2 | +| 37 | 0.5703 | 0.6875 | 0.000000 | -0.0000 | 1077.8 | 785.6 | +| 38 | 0.4570 | 0.6094 | 0.000000 | -0.0000 | 1281.5 | 984.5 | +| 39 | 0.4219 | 0.5625 | 0.000000 | -0.0000 | 1220.2 | 969.3 | +| 40 | 0.4766 | 0.5938 | 0.000000 | 0.0000 | 1608.5 | 1354.9 | +| 41 | 0.4980 | 0.5781 | 0.000000 | -0.0000 | 1037.8 | 802.5 | +| 42 | 0.3770 | 0.4531 | 0.000000 | 0.0000 | 1385.1 | 1098.6 | +| 43 | 0.4043 | 0.4844 | 0.000000 | -0.0000 | 1474.0 | 1198.9 | +| 44 | 0.4980 | 0.6250 | 0.000000 | -0.0000 | 995.3 | 726.5 | +| 45 | 0.4707 | 0.5156 | 0.000000 | 0.0000 | 1030.1 | 747.8 | +| 46 | 0.4766 | 0.5625 | 0.000000 | 0.0000 | 1116.6 | 848.4 | +| 47 | 0.5098 | 0.6250 | 0.000000 | 0.0000 | 1436.6 | 1153.8 | +| 48 | 0.3965 | 0.5000 | 0.000000 | -0.0000 | 1388.9 | 1118.5 | +| 49 | 0.3613 | 0.4844 | 0.000000 | 0.0000 | 1674.2 | 1335.1 | +| 50 | 0.4473 | 0.5469 | 0.000000 | 0.0000 | 1060.2 | 789.3 | +| 51 | 0.3848 | 0.4531 | 0.000000 | 0.0000 | 1686.7 | 1378.9 | +| 52 | 0.4102 | 0.5312 | 0.000000 | -0.0000 | 1600.4 | 1319.3 | +| 53 | 0.4473 | 0.5312 | 0.000000 | 0.0000 | 1082.9 | 829.7 | +| 54 | 0.3887 | 0.5156 | 0.000000 | 0.0000 | 1827.2 | 1523.5 | +| 55 | 0.4004 | 0.5156 | 0.000000 | -0.0000 | 1484.0 | 1185.3 | +| 56 | 0.4121 | 0.5156 | 0.000000 | -0.0000 | 1375.8 | 1104.5 | + +### job_630130 + +| Step | Reward | Pass@8 | KL | Loss | Step Time (s) | Gen Wait (s) | +|------|--------|--------|-----|------|---------------|-------------| +| 57 | 0.2051 | 0.3898 | 0.000000 | -0.0007 | 10105.4 | 9825.4 | +| 58 | 0.2266 | 0.5000 | 0.000000 | -0.0008 | 3407.9 | 3181.4 | +| 59 | 0.2520 | 0.5000 | 0.000000 | -0.0015 | 3636.1 | 3468.6 | +| 60 | 0.2129 | 0.4531 | 0.000000 | -0.0008 | 3855.0 | 3710.5 | +| 61 | 0.4590 | 0.7188 | 0.000000 | -0.0015 | 1882.4 | 1715.1 | +| 62 | 0.5117 | 0.6094 | 0.000000 | -0.0003 | 1318.3 | 1070.7 | +| 63 | 0.4746 | 0.5469 | 0.000000 | -0.0001 | 1359.1 | 1102.8 | +| 64 | 0.3809 | 0.4844 | 0.000000 | 0.0000 | 1636.6 | 1345.1 | +| 65 | 0.4668 | 0.6094 | 0.000000 | 0.0000 | 1536.8 | 1260.2 | +| 66 | 0.3965 | 0.5781 | 0.000000 | -0.0000 | 1507.0 | 1239.0 | +| 67 | 0.4414 | 0.5625 | 0.000000 | -0.0000 | 1571.3 | 1328.7 | +| 68 | 0.4180 | 0.5312 | 0.000000 | -0.0000 | 1442.2 | 1172.7 | +| 69 | 0.5449 | 0.6562 | 0.000000 | -0.0000 | 1413.2 | 1162.3 | +| 70 | 0.4102 | 0.5312 | 0.000000 | -0.0000 | 1779.8 | 1503.9 | + +### job_661735 + +| Step | Reward | Pass@8 | KL | Loss | Step Time (s) | Gen Wait (s) | +|------|--------|--------|-----|------|---------------|-------------| +| 71 | 0.5566 | 0.5965 | 0.000000 | -0.0000 | 3675.9 | 3449.1 | +| 72 | 0.2773 | 0.4219 | 0.000000 | 0.0000 | 1642.3 | 1345.3 | +| 73 | 0.3320 | 0.4062 | 0.000000 | 0.0000 | 875.4 | 618.9 | +| 74 | 0.4941 | 0.5938 | 0.000000 | -0.0000 | 1734.0 | 1436.9 | +| 75 | 0.3906 | 0.5625 | 0.000000 | -0.0000 | 1567.8 | 1296.9 | +| 76 | 0.3164 | 0.4531 | 0.000000 | 0.0000 | 1415.5 | 1155.5 | +| 77 | 0.4043 | 0.4688 | 0.000000 | -0.0001 | 1161.9 | 906.7 | +| 78 | 0.2266 | 0.3750 | 0.000000 | 0.0000 | 1837.8 | 1593.9 | + +## Timing Analysis + +### Average Time Breakdown (% of step time) + +| Component | Avg % of Step Time | +|-----------|-------------------| +| wait_for_generation_buffer | 80.8% | +| run_training | 17.3% | +| train_critic_and_policy | 14.8% | +| policy_train | 14.8% | +| fwd_logprobs_values_reward | 2.5% | +| sync_weights | 1.6% | +| save_hf_model | 1.0% | +| save_checkpoints | 0.9% | +| convert_to_training_input | 0.3% | +| cleanup_old_checkpoints | 0.2% | +| compute_advantages_and_returns | 0.0% | + +## Cross-Log Comparison + +| Log | Avg Reward | Pass@8 | Step Time (s) | Gen Wait Time (s) | Avg Tokens | Staleness | +|-----|------|------|------|------|------|------| +| job_630124 | 0.4467 | 0.5575 | 1476.9481 | 1198.3201 | 5107.1387 | 1.7701 | +| job_630127 | 0.4329 | 0.5431 | 1435.2019 | 1152.8499 | 5301.5774 | 1.8175 | +| job_630130 | 0.3858 | 0.5479 | 2603.6675 | 2363.3124 | 4025.6459 | 1.5424 | +| job_661735 | 0.3747 | 0.4847 | 1738.8407 | 1475.4061 | 4821.2654 | 1.9980 | + +## vLLM Inference Engine Analysis + +Metrics from vLLM stat loggers (V1LoggingStatLoggerFixed). + +> **Note**: Ray deduplicates similar log messages with `[repeated Nx across cluster]`, +> so we typically capture stats from one engine per timestamp. The stats shown are +> **per-engine** values. Multiply by num_inference_engines for cluster-wide estimates. + +### Summary by Log (Per-Engine Stats) + +| Log | Avg Running/Engine | Avg Waiting/Engine | Avg Gen Throughput/Engine | Avg KV Cache % | Avg Prefix Hit % | +|-----|-------------------|-------------------|--------------------------|----------------|------------------| +| job_630124 | 11.5 | 0.4 | 494.3 tok/s | 51.4% | 67.8% | +| job_630127 | 12.0 | 0.6 | 510.3 tok/s | 53.0% | 69.6% | +| job_630130 | 6.4 | 0.0 | 287.3 tok/s | 28.1% | 85.0% | +| job_661734 | 8.1 | 0.0 | 344.0 tok/s | 34.5% | 83.8% | +| job_661735 | 10.2 | 0.2 | 442.6 tok/s | 44.3% | 69.2% | + +### Utilization Analysis (Per-Engine) + +Key indicators of inference engine utilization: + +- **Running requests/engine**: Concurrent requests being processed by each engine +- **Waiting requests**: Requests queued (0 = engine not saturated, has spare capacity) +- **Generation throughput**: Decode tokens/sec per engine + - 8B model on H100 can do **1000+ tok/s** when saturated + - If seeing <300 tok/s with 0 waiting, engine is **starved for requests** + +#### job_630124 + +- **Running requests/engine**: avg=11.5, max=24 +- **Waiting requests**: avg=0.4, max=43 +- **Generation throughput/engine**: avg=494.3 tok/s, max=984.1 tok/s +- **KV cache usage**: avg=51.4% +- **Prefix cache hit rate**: avg=67.8% +- ✅ **Well-utilized**: Engines saturated (waiting > 0) + +#### job_630127 + +- **Running requests/engine**: avg=12.0, max=24 +- **Waiting requests**: avg=0.6, max=41 +- **Generation throughput/engine**: avg=510.3 tok/s, max=1005.9 tok/s +- **KV cache usage**: avg=53.0% +- **Prefix cache hit rate**: avg=69.6% +- ✅ **Well-utilized**: Engines saturated (waiting > 0) + +#### job_630130 + +- **Running requests/engine**: avg=6.4, max=24 +- **Waiting requests**: avg=0.0, max=19 +- **Generation throughput/engine**: avg=287.3 tok/s, max=952.9 tok/s +- **KV cache usage**: avg=28.1% +- **Prefix cache hit rate**: avg=85.0% +- ✅ **Well-utilized**: Engines saturated (waiting > 0) + +#### job_661734 + +- **Running requests/engine**: avg=8.1, max=24 +- **Waiting requests**: avg=0.0, max=4 +- **Generation throughput/engine**: avg=344.0 tok/s, max=862.0 tok/s +- **KV cache usage**: avg=34.5% +- **Prefix cache hit rate**: avg=83.8% +- ✅ **Well-utilized**: Engines saturated (waiting > 0) + +#### job_661735 + +- **Running requests/engine**: avg=10.2, max=24 +- **Waiting requests**: avg=0.2, max=18 +- **Generation throughput/engine**: avg=442.6 tok/s, max=962.9 tok/s +- **KV cache usage**: avg=44.3% +- **Prefix cache hit rate**: avg=69.2% +- ✅ **Well-utilized**: Engines saturated (waiting > 0) + +## Trial-Level Analysis (from result.json) + +Total trials parsed: 38123 + +### Turn Count Statistics + +| Metric | Value | +|--------|-------| +| Mean | 5.0 | +| Median | 5.0 | +| Std | 2.4 | +| Min | 1 | +| Max | 33 | +| Count | 37976 | + +### Exception Distribution + +| Exception Type | Count | % | +|---------------|-------|---| +| No exception | 26113 | 68.5% | +| AgentTimeoutError | 10372 | 27.2% | +| VerifierTimeoutError | 802 | 2.1% | +| ContextLengthExceededError | 679 | 1.8% | +| AgentSetupTimeoutError | 105 | 0.3% | +| RuntimeError | 41 | 0.1% | +| DaytonaError | 7 | 0.0% | +| DaytonaAuthenticationError | 4 | 0.0% | + +### Turn Count by Exception Type + +| Exception Type | Mean Turns | Median Turns | Count | +|---------------|-----------|-------------|-------| +| No exception | 5.6 | 5.0 | 26113 | +| VerifierTimeoutError | 5.6 | 5.0 | 802 | +| ContextLengthExceededError | 3.8 | 4.0 | 679 | +| AgentTimeoutError | 3.4 | 3.0 | 10372 | +| DaytonaError | 3.2 | 4.0 | 6 | +| DaytonaAuthenticationError | 1.8 | 1.5 | 4 | + +### Turn Count by Outcome + +| Outcome | Mean Turns | Median Turns | Count | +|---------|-----------|-------------|-------| +| Success | 5.3 | 5.0 | 16218 | +| Failure | 4.8 | 5.0 | 19449 | + +### Reward Summary + +- Mean reward: 0.4547 +- Success rate: 45.5% +- Trials with reward data: 35667 + diff --git a/training_logs/20260612_220124_metrics_table.csv b/training_logs/20260612_220124_metrics_table.csv new file mode 100644 index 0000000..0c4a3a1 --- /dev/null +++ b/training_logs/20260612_220124_metrics_table.csv @@ -0,0 +1,79 @@ +log_file,async/discard_rate,async/discarded_count,async/effective_batch_groups,async/effective_batch_samples,async/staleness_max,async/staleness_mean,async/staleness_min,async/staleness_ratio,generate/avg_num_tokens,generate/avg_tokens_non_zero_rewards,generate/avg_tokens_zero_rewards,generate/max_num_tokens,generate/min_num_tokens,generate/std_num_tokens,loss/avg_final_rewards,loss/avg_raw_advantages,loss/avg_raw_advantages_abs,policy/final_loss,policy/log_ratio_abs_max,policy/log_ratio_abs_mean,policy/log_ratio_abs_p99,policy/log_ratio_abs_pos00,policy/log_ratio_abs_pos10,policy/log_ratio_abs_pos20,policy/log_ratio_abs_pos30,policy/log_ratio_abs_pos40,policy/log_ratio_abs_pos50,policy/log_ratio_abs_pos60,policy/log_ratio_abs_pos70,policy/log_ratio_abs_pos80,policy/log_ratio_abs_pos90,policy/n_tokens_dp_gt_10pct,policy/n_tokens_dp_gt_1pct,policy/n_tokens_dp_gt_50pct,policy/policy_entropy,policy/policy_loss,policy/policy_lr,policy/policy_update_steps,policy/ppo_clip_ratio,policy/raw_grad_norm,reward/avg_pass_at_8,reward/avg_raw_reward,system/process_rss_gb,system/process_vms_gb,system/ram_available_gb,system/ram_percent,system/ram_total_gb,system/ram_used_gb,timing/compute_advantages_and_returns,timing/convert_to_training_input,timing/fwd_logprobs_values_reward,timing/policy_train,timing/run_training,timing/step,timing/sync_weights,timing/train_critic_and_policy,timing/wait_for_generation_buffer,trainer/epoch,trainer/global_step,batch_errors/total_batches,batch_errors/total_instances,batch_errors/total_successful,batch_errors/total_failed,batch_errors/total_masked,batch_errors/avg_RuntimeError,batch_errors/total_RuntimeError,batch_errors/avg_VerifierTimeoutError,batch_errors/total_VerifierTimeoutError,batch_errors/avg_AgentTimeoutError,batch_errors/total_AgentTimeoutError,batch_errors/avg_InvalidChatHistory,batch_errors/total_InvalidChatHistory,batch_errors/avg_ContextLengthExceededError,batch_errors/total_ContextLengthExceededError,timing/cleanup_old_checkpoints,timing/save_checkpoints,timing/save_hf_model,batch_errors/avg_DaytonaError,batch_errors/total_DaytonaError,batch_errors/avg_AgentSetupTimeoutError,batch_errors/total_AgentSetupTimeoutError,batch_errors/avg_DaytonaAuthenticationError,batch_errors/total_DaytonaAuthenticationError,global_step 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+pymethods2test-0049,pymethods2test-0049__XscrBkF,4.0,6471.0,2399.0,AgentTimeoutError,0.0,900.005881 +pymethods2test-2973,pymethods2test-2973__cCaUgTT,5.0,28711.0,3228.0,,0.0,232.507175 +pymethods2test-2321,pymethods2test-2321__ApU8HW4,3.0,3579.0,1416.0,AgentTimeoutError,1.0,900.005988 +pymethods2test-3554,pymethods2test-3554__Yw7pfFw,6.0,36471.0,6126.0,ContextLengthExceededError,,845.232548 +pymethods2test-1475,pymethods2test-1475__8j3Y9xW,7.0,53299.0,12909.0,,1.0,784.693784 +pymethods2test-1559,pymethods2test-1559__5wESaZe,4.0,7678.0,4351.0,AgentTimeoutError,0.0,900.005957 +pymethods2test-3311,pymethods2test-3311__bHufhLR,3.0,5475.0,1203.0,,1.0,179.891137 +pymethods2test-4854,pymethods2test-4854__K3weUWA,4.0,11466.0,2853.0,,0.0,222.038613 +pymethods2test-4825,pymethods2test-4825__SyGLprJ,3.0,6009.0,3396.0,ContextLengthExceededError,,869.279603 +pymethods2test-0835,pymethods2test-0835__DPCdA5t,4.0,10464.0,2459.0,,0.0,420.510666 +pymethods2test-1528,pymethods2test-1528__EzGgPqs,7.0,26609.0,3709.0,,1.0,587.102581 +pymethods2test-2550,pymethods2test-2550__6HdtD8n,5.0,24833.0,4509.0,AgentTimeoutError,,906.142564 +pymethods2test-3459,pymethods2test-3459__xJ9eTCu,4.0,9534.0,1868.0,,1.0,162.577264 +pymethods2test-4637,pymethods2test-4637__mj6625H,4.0,7338.0,1119.0,,1.0,328.565596 +pymethods2test-4176,pymethods2test-4176__iYNnUbx,6.0,21110.0,3772.0,,0.0,312.989693 +pymethods2test-3831,pymethods2test-3831__oyYbeVf,6.0,21621.0,3205.0,,1.0,316.863398 +pymethods2test-0809,pymethods2test-0809__aDfeRLM,4.0,6841.0,1195.0,,1.0,263.879694 +pymethods2test-3144,pymethods2test-3144__JCZJvzL,5.0,14868.0,3159.0,AgentTimeoutError,0.0,900.009952 +pymethods2test-4148,pymethods2test-4148__wgMVKRS,5.0,14980.0,2438.0,,0.0,840.625156 +pymethods2test-4468,pymethods2test-4468__n7A4wYM,8.0,40482.0,5698.0,AgentTimeoutError,0.0,900.263012 +pymethods2test-0133,pymethods2test-0133__cCfLbAx,2.0,1025.0,1767.0,AgentTimeoutError,0.0,900.479432 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+pymethods2test-2128,pymethods2test-2128__pJNtvWz,7.0,35142.0,4983.0,,1.0,462.106653 +pymethods2test-1978,pymethods2test-1978__x7fh6kx,5.0,17962.0,3024.0,,0.0,463.665836 +pymethods2test-2572,pymethods2test-2572__EJjudxq,5.0,12622.0,1945.0,,1.0,303.063094 +pymethods2test-1495,pymethods2test-1495__vcymeXi,6.0,16096.0,3984.0,AgentTimeoutError,0.0,900.225735 +pymethods2test-2221,pymethods2test-2221__SYRHNYn,4.0,7670.0,3605.0,AgentTimeoutError,0.0,952.857379 +pymethods2test-2720,pymethods2test-2720__cFrQxAA,7.0,40895.0,5331.0,,0.0,502.964662 +pymethods2test-2524,pymethods2test-2524__Lu7N3rR,7.0,37338.0,6254.0,,0.0,635.567521 +pymethods2test-3226,pymethods2test-3226__NLckMse,8.0,36463.0,5410.0,,0.0,569.901582 +pymethods2test-1628,pymethods2test-1628__yWeAjbW,1.0,0.0,0.0,AgentTimeoutError,0.0,936.859631 +pymethods2test-1466,pymethods2test-1466__KgcdrMV,5.0,9775.0,2229.0,AgentTimeoutError,1.0,900.005026 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+pymethods2test-1447,pymethods2test-1447__PieBokt,4.0,7335.0,1166.0,,1.0,32.563644 +pymethods2test-4407,pymethods2test-4407__4kDmiDs,1.0,0.0,0.0,AgentTimeoutError,0.0,900.004637 +pymethods2test-1879,pymethods2test-1879__VoPQve6,1.0,0.0,0.0,AgentTimeoutError,0.0,900.019435 +pymethods2test-1759,pymethods2test-1759__goaUrUT,4.0,17311.0,5653.0,,0.0,543.583575 +pymethods2test-4373,pymethods2test-4373__3Poj8hE,4.0,5799.0,1357.0,AgentTimeoutError,0.0,900.005098 +pymethods2test-4355,pymethods2test-4355__tm6GtTR,4.0,10637.0,6728.0,ContextLengthExceededError,,886.626449 +pymethods2test-4964,pymethods2test-4964__YsFaaP3,5.0,16839.0,4003.0,,1.0,392.979371 +pymethods2test-1492,pymethods2test-1492__hyeCBAW,4.0,9896.0,2155.0,,1.0,55.909571 +pymethods2test-2508,pymethods2test-2508__WtjpbUf,4.0,10058.0,2260.0,,1.0,312.421081 +pymethods2test-1640,pymethods2test-1640__xLiLqe9,5.0,21701.0,3640.0,,0.0,366.435664 +pymethods2test-2526,pymethods2test-2526__CjxuiwX,8.0,31676.0,3777.0,,1.0,100.687176 +pymethods2test-0752,pymethods2test-0752__wjVgirW,6.0,29583.0,6402.0,,1.0,718.425457 +pymethods2test-3378,pymethods2test-3378__NLijza5,4.0,14658.0,3493.0,VerifierTimeoutError,,208.448961 +pymethods2test-1217,pymethods2test-1217__iHn7QK9,4.0,9908.0,2034.0,,1.0,180.895063 +pymethods2test-2493,pymethods2test-2493__f7FaDx3,9.0,58203.0,7519.0,,0.0,537.903214 +pymethods2test-2657,pymethods2test-2657__DZ4WEPf,8.0,40336.0,5401.0,,0.0,515.896364 +pymethods2test-2368,pymethods2test-2368__zKbjyDn,5.0,10858.0,1393.0,,1.0,171.895158 +pymethods2test-0105,pymethods2test-0105__frKpugR,15.0,71858.0,4488.0,,0.0,848.550709 +pymethods2test-1911,pymethods2test-1911__mVigtf6,3.0,3437.0,1185.0,AgentTimeoutError,,900.004721 +pymethods2test-2889,pymethods2test-2889__oWmVCfu,3.0,3141.0,2276.0,AgentTimeoutError,0.0,900.012411 +pymethods2test-1693,pymethods2test-1693__tjCv7YF,7.0,19419.0,2364.0,,1.0,375.794042 +pymethods2test-1231,pymethods2test-1231__nTvoPu2,8.0,43186.0,6340.0,,1.0,485.640421 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20:52:12,1,1,0.0,466.6,11,0,0.0,466.6,11.0,0.0,36.2,90.0,11,11,466.6,466.6 +job_661735,06-08 20:52:17,1,1,0.0,349.6,8,0,0.0,349.6,8.0,0.0,35.1,41.1,8,8,349.6,349.6 +job_661735,06-08 20:52:22,1,1,0.0,498.8,11,0,0.0,498.8,11.0,0.0,37.5,90.0,11,11,498.8,498.8 +job_661735,06-08 20:52:27,1,1,61.9,514.3,11,0,61.9,514.3,11.0,0.0,42.4,88.4,11,11,514.3,514.3 +job_661735,06-08 20:52:32,1,1,0.0,544.5,12,0,0.0,544.5,12.0,0.0,43.8,64.4,12,12,544.5,544.5 +job_661735,06-08 20:52:37,1,1,121.2,430.8,10,0,121.2,430.8,10.0,0.0,34.7,38.0,10,10,430.8,430.8 +job_661735,06-08 20:52:42,1,1,9.8,457.5,11,0,9.8,457.5,11.0,0.0,35.4,38.0,11,11,457.5,457.5 +job_661735,06-08 20:52:47,1,1,9.6,453.9,11,0,9.6,453.9,11.0,0.0,35.0,38.1,11,11,453.9,453.9 +job_661735,06-08 20:52:52,1,1,0.0,424.6,10,0,0.0,424.6,10.0,0.0,39.9,90.0,10,10,424.6,424.6 +job_661735,06-08 20:52:57,1,1,0.0,424.9,10,0,0.0,424.9,10.0,0.0,40.5,90.0,10,10,424.9,424.9 +job_661735,06-08 20:53:02,1,1,0.0,425.0,10,0,0.0,425.0,10.0,0.0,41.1,90.0,10,10,425.0,425.0 +job_661735,06-08 20:53:07,1,1,91.5,442.9,9,0,91.5,442.9,9.0,0.0,28.7,88.0,9,9,442.9,442.9 +job_661735,06-08 20:53:12,1,1,72.2,446.4,10,0,72.2,446.4,10.0,0.0,29.4,88.0,10,10,446.4,446.4 +job_661735,06-08 20:53:17,1,1,0.0,491.6,10,0,0.0,491.6,10.0,0.0,30.1,88.0,10,10,491.6,491.6 +job_661735,06-08 20:53:22,1,1,23.5,488.4,9,0,23.5,488.4,9.0,0.0,30.6,88.1,9,9,488.4,488.4 +job_661735,06-08 20:53:27,1,1,149.1,433.3,9,0,149.1,433.3,9.0,0.0,32.6,88.2,9,9,433.3,433.3 +job_661735,06-08 20:53:32,1,1,0.0,660.1,15,0,0.0,660.1,15.0,0.0,42.9,38.5,15,15,660.1,660.1 +job_661735,06-08 20:53:37,1,1,0.0,614.4,13,0,0.0,614.4,13.0,0.0,42.7,38.5,13,13,614.4,614.4 +job_661735,06-08 20:53:42,1,1,0.0,569.2,13,0,0.0,569.2,13.0,0.0,51.4,90.3,13,13,569.2,569.2 +job_661735,06-08 20:53:47,1,1,647.0,568.3,14,0,647.0,568.3,14.0,0.0,53.8,90.2,14,14,568.3,568.3 +job_661735,06-08 20:53:52,1,1,0.0,584.6,12,0,0.0,584.6,12.0,0.0,52.0,90.2,12,12,584.6,584.6 +job_661735,06-08 20:53:57,1,1,0.0,528.1,12,0,0.0,528.1,12.0,0.0,52.8,90.2,12,12,528.1,528.1 +job_661735,06-08 20:54:02,1,1,134.1,524.9,12,0,134.1,524.9,12.0,0.0,52.7,90.2,12,12,524.9,524.9 +job_661735,06-08 20:54:07,1,1,0.0,525.7,12,0,0.0,525.7,12.0,0.0,53.5,90.2,12,12,525.7,525.7 +job_661735,06-08 20:54:12,1,1,144.0,530.6,13,0,144.0,530.6,13.0,0.0,56.2,90.2,13,13,530.6,530.6 +job_661735,06-08 20:54:17,1,1,9.0,575.6,14,0,9.0,575.6,14.0,0.0,57.1,90.2,14,14,575.6,575.6 +job_661735,06-08 20:54:22,1,1,60.1,685.7,16,0,60.1,685.7,16.0,0.0,58.6,90.2,16,16,685.7,685.7 +job_661735,06-08 20:54:27,1,1,0.0,506.6,10,0,0.0,506.6,10.0,0.0,31.5,42.4,10,10,506.6,506.6 +job_661735,06-08 20:54:32,1,1,0.0,481.3,10,0,0.0,481.3,10.0,0.0,32.2,42.4,10,10,481.3,481.3 +job_661735,06-08 20:54:38,1,1,0.0,481.1,10,0,0.0,481.1,10.0,0.0,32.9,42.4,10,10,481.1,481.1 +job_661735,06-08 20:54:43,1,1,0.0,481.7,10,0,0.0,481.7,10.0,0.0,33.5,42.4,10,10,481.7,481.7 +job_661735,06-08 20:54:48,1,1,0.0,480.7,10,0,0.0,480.7,10.0,0.0,34.2,42.4,10,10,480.7,480.7 +job_661735,06-08 20:54:53,1,1,217.1,519.1,11,0,217.1,519.1,11.0,0.0,36.7,42.5,11,11,519.1,519.1 +job_661735,06-08 20:54:58,1,1,0.0,628.5,17,0,0.0,628.5,17.0,0.0,62.4,89.9,17,17,628.5,628.5 +job_661735,06-08 20:55:03,1,1,240.4,640.9,19,0,240.4,640.9,19.0,0.0,66.7,89.9,19,19,640.9,640.9 +job_661735,06-08 20:55:08,1,1,0.0,700.9,19,0,0.0,700.9,19.0,0.0,67.7,89.9,19,19,700.9,700.9 +job_661735,06-08 20:55:13,1,1,0.0,678.0,18,0,0.0,678.0,18.0,0.0,66.5,89.9,18,18,678.0,678.0 +job_661735,06-08 20:55:18,1,1,0.0,664.0,18,0,0.0,664.0,18.0,0.0,67.4,89.9,18,18,664.0,664.0 +job_661735,06-08 20:55:23,1,1,0.0,646.3,17,0,0.0,646.3,17.0,0.0,66.7,89.9,17,17,646.3,646.3 +job_661735,06-08 20:55:28,1,1,0.0,626.3,17,0,0.0,626.3,17.0,0.0,67.6,89.9,17,17,626.3,626.3 +job_661735,06-08 20:55:33,1,1,0.0,625.3,17,0,0.0,625.3,17.0,0.0,68.5,89.9,17,17,625.3,625.3 +job_661735,06-08 20:55:38,1,1,0.0,627.0,17,0,0.0,627.0,17.0,0.0,69.4,89.9,17,17,627.0,627.0 +job_661735,06-08 20:55:43,1,1,0.0,625.3,17,0,0.0,625.3,17.0,0.0,70.2,89.9,17,17,625.3,625.3 +job_661735,06-08 20:55:48,1,1,0.0,626.0,17,0,0.0,626.0,17.0,0.0,71.1,89.9,17,17,626.0,626.0 +job_661735,06-08 20:55:53,1,1,0.0,624.0,17,0,0.0,624.0,17.0,0.0,72.0,89.9,17,17,624.0,624.0 +job_661735,06-08 20:55:54,1,1,0.0,217.5,5,0,0.0,217.5,5.0,0.0,18.6,90.9,5,5,217.5,217.5 diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630124.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630124.out new file mode 100644 index 0000000..ecf07f2 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630124.out @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57544589430cce1ff125aad5db8ab73a85bdbdec7dcaa61c6c4e48114e44a561 +size 44390800 diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630125.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630125.out new file mode 100644 index 0000000..b9c350c --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630125.out @@ -0,0 +1,1552 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Activating RL environment: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl +Python executable: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python path check: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +[ray] RAY_TMPDIR=/tmp/ray/ray_630125 +[triton_cache] Triton cache: /tmp/triton_cache_feuer1_630125 +[triton_cache] TorchInductor cache: /tmp/torchinductor_cache_feuer1_630125 +[proxy] ✓ Found proxychains binary at /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 +[proxy] Setting up SSH tunnel to jpbl-s01-01 +[proxy] SSH key: /e/home/jusers/feuer1/jupiter/.ssh/authorized_keys/id_ed25519_jsc +[proxy] Tunnel port: 7003 +[proxy] Node IP: 10.128.32.34 (workers will connect here) +[proxy] ✓ SSH tunnel started successfully +[proxy] ✓ Generated proxychains config at /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630125.conf +[proxy] - Internal traffic (10.x.x.x, 172.x.x.x, 169.254.x.x) → DIRECT +[proxy] - External traffic (internet) → PROXY via tunnel +[proxy] ✓ Daytona timeout settings configured +[proxy] Testing proxy connectivity... +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630125.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[proxy] ✓ Proxy connectivity test passed (huggingface.co reachable via wrapped binary) +[proxy] ⚠ Tunnel not accessible at 10.128.32.34:7003 (workers may fail) +[proxy] ✓ Proxy setup complete (using wrapped binary for Ray workers) +[container_runtime] Using cloud backend: daytona (no local container setup) +=== Universal RL Training Runner === +Config: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/configs/ablation-pymethods2test-seqmean-arm0_rl_config.json +Working directory: /e/scratch/jureap59/feuer1/OpenThoughts-Agent +Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python version: Python 3.12.12 +UV_USE_IO_URING: 0 +Proxy: DISABLED (direct internet or not configured) +======================================== +=== RLJobRunner: ablation-pymethods2test-seqmean-arm0 === +[wandb_utils] Fixing permissions on: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +[wandb_utils] WandB directory ready: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb + HF_TOKEN=****pDbg + HF_HUB_CACHE=/e/data1/datasets/playground/ot-baf/hf_hub + SUPABASE_URL=https://rpzmyuapoqilpghynmza.s... (direct Supabase config) +Environment configured: + TENSOR_PARALLEL_SIZE=1 + NUM_INFERENCE_ENGINES=56 + POLICY_NUM_NODES=14 + WANDB_DIR=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +Starting Ray cluster with 14 nodes, 4 GPUs/node +Cleaning up existing Ray instances... +=== Starting Ray Cluster === + Nodes: 14 + GPUs per node: 4 + CPUs per node: 288 + Head node: jpbo-041-34 (10.128.32.34) + Ray port: 6379 +============================ + Starting Ray head on jpbo-041-34 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_head_jpbo-041-34.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.34 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-34 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --head --node-ip-address=10.128.32.34 --port=6379 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray head on jpbo-041-34 + Starting Ray worker on jpbo-041-36 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-36.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.36 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-36 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.36 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 1 on jpbo-041-36 + Starting Ray worker on jpbo-041-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.37 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.37 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 2 on jpbo-041-37 + Starting Ray worker on jpbo-041-38 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-38.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.38 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-38 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.38 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 3 on jpbo-041-38 + Starting Ray worker on jpbo-041-39 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-39.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.39 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-39 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.39 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 4 on jpbo-041-39 + Starting Ray worker on jpbo-041-40 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-40.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.40 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-40 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.40 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 5 on jpbo-041-40 + Starting Ray worker on jpbo-041-41 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-41.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.41 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-41 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.41 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 6 on jpbo-041-41 + Starting Ray worker on jpbo-041-42 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-42.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.42 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-42 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.42 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 7 on jpbo-041-42 + Starting Ray worker on jpbo-041-43 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-43.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.43 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-43 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.43 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 8 on jpbo-041-43 + Starting Ray worker on jpbo-041-44 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-44.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.44 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-44 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.44 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 9 on jpbo-041-44 + Starting Ray worker on jpbo-041-45 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-45.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.45 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-45 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.45 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 10 on jpbo-041-45 + Starting Ray worker on jpbo-041-46 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-46.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.46 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-46 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.46 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 11 on jpbo-041-46 + Starting Ray worker on jpbo-041-47 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-47.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.47 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-47 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.47 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 12 on jpbo-041-47 + Starting Ray worker on jpbo-041-48 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-48.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.48 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-48 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.34:6379 --node-ip-address=10.128.32.48 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 13 on jpbo-041-48 + Waiting for cluster (56 GPUs, 14 nodes)... +Connecting to Ray at 10.128.32.34:6379 (expecting 14 nodes, 56.0 GPUs) +Ray connection established, polling for resources... +[Ray wait] nodes=14/14 GPUs=56.0/56.0 resources={'object_store_memory': 601295421440.0, 'memory': 10695797243904.0, 'GPU': 56.0, 'CPU': 4032.0, 'accelerator_type:GH200': 14.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.39': 1.0, 'node:10.128.32.37': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.36': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.41': 1.0, 'node:10.128.32.48': 1.0, 'node:10.128.32.47': 1.0} +✓ Ray cluster ready +=== Ray Cluster Ready === + Address: 10.128.32.34:6379 + Total GPUs: 56 +========================= +Ray cluster ready at 10.128.32.34:6379 +Total GPUs available: 56 +[RLJobRunner] Pinggy check: url=False, token=False, needs_tunnel=False (agent=terminus-2, env=daytona) +[RLJobRunner] No Pinggy tunnel needed, using local vLLM + +Running SkyRL: + Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python + Entrypoint: examples.terminal_bench.entrypoints.main_tbench + Args: 120 Hydra arguments + Working dir: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train +Using proxychains binary: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 + +Executing command with srun: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f $PROXYCHAINS_CONF_FILE /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python -m examples.terminal_bench.entrypoints.main_tbench +terminal_bench_config=terminal_bench trainer.strategy=fsdp2 trainer.algorithm.advantage_estimator=rloo_n trainer.algorithm.use_kl_loss=false trainer.algorithm.kl_loss_coef=0.0 trainer.algorithm.eps_clip_low=0.2 trainer.algorithm.eps_clip_high=0.05 trainer.algorithm.loss_reduction=sequence_mean trainer.epochs=2 trainer.max_steps=80 trainer.update_epochs_per_batch=1 trainer.train_batch_size=64 trainer.policy_mini_batch_size=64 trainer.eval_batch_size=64 trainer.micro_forward_batch_size_per_gpu=4 trainer.micro_train_batch_size_per_gpu=1 trainer.max_prompt_length=999999 trainer.eval_interval=999999 trainer.eval_before_train=false trainer.ckpt_interval=2 trainer.resume_mode=latest trainer.hf_save_interval=5 ++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0 ++trainer.hf_hub_private=false ++trainer.hf_hub_revision=main ++trainer.enable_db_registration=false trainer.project_name=OpenThoughts-Agent trainer.log_level=INFO trainer.tracker_commit_each_step=true trainer.logger=console trainer.run_name=ablation-pymethods2test-seqmean-arm0 trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports trainer.policy.optimizer_config.lr=8e-6 trainer.policy.optimizer_config.weight_decay=0.0 trainer.policy.optimizer_config.adam_betas=[0.9,0.999] trainer.policy.optimizer_config.max_grad_norm=0.9 trainer.policy.fsdp_config.cpu_offload=false trainer.policy.fsdp_config.reshard_after_forward=true trainer.policy.fsdp_config.fsdp_size=4 trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 trainer.ref.fsdp_config.cpu_offload=false trainer.ref.fsdp_config.reshard_after_forward=true trainer.ref.fsdp_config.fsdp_size=4 trainer.placement.colocate_all=false trainer.placement.policy_num_nodes=2 trainer.placement.ref_num_nodes=2 trainer.placement.policy_num_gpus_per_node=4 trainer.placement.ref_num_gpus_per_node=4 trainer.fully_async.max_staleness_steps=16 trainer.fully_async.num_parallel_generation_workers=338 generator.backend=vllm generator.timeout_multiplier=1.0 generator.model_dtype=bfloat16 generator.inference_engine_tensor_parallel_size=1 generator.num_inference_engines=48 generator.n_samples_per_prompt=8 generator.eval_n_samples_per_prompt=8 generator.gpu_memory_utilization=0.75 generator.max_num_seqs=24 generator.max_num_batched_tokens=65536 generator.enable_prefix_caching=true generator.enable_chunked_prefill=true generator.run_engines_locally=true generator.weight_sync_backend=nccl generator.async_engine=true generator.batched=false generator.enable_http_endpoint=true generator.enable_ray_prometheus_stats=false generator.vllm_stats_interval=1 generator.append_eos_token_after_stop_str_in_multi_turn=true generator.max_turns=999999 generator.sampling_params.max_generate_length=4096 generator.sampling_params.temperature=0.7 generator.sampling_params.top_p=0.95 generator.sampling_params.top_k=20 ++generator.engine_init_kwargs.max_model_len=32768 ++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2 ++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"] data.val_data=[] +terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs +terminal_bench_config.harbor.name=terminus-2 +terminal_bench_config.harbor.max_episodes=999999 +terminal_bench_config.harbor.enable_summarize=false +terminal_bench_config.harbor.store_all_messages=true +terminal_bench_config.harbor.trajectory_config.raw_content=true +terminal_bench_config.harbor.enable_episode_logging=false +terminal_bench_config.harbor.record_terminal_session=false +terminal_bench_config.harbor.enable_pane_logging=false +terminal_bench_config.harbor.strict_json_parser=true +terminal_bench_config.harbor.interleaved_thinking=true +terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true +terminal_bench_config.harbor.override_timeout_sec=900 +terminal_bench_config.harbor.override_cpus=1 +terminal_bench_config.harbor.override_memory_mb=2048 +terminal_bench_config.harbor.override_storage_mb=2048 +terminal_bench_config.harbor.auto_snapshot=true +terminal_bench_config.harbor.verifier_override_timeout_sec=120 +terminal_bench_config.harbor.max_retries=3 +terminal_bench_config.harbor.min_wait_sec=60.0 +terminal_bench_config.harbor.max_wait_sec=600.0 +terminal_bench_config.harbor.wait_multiplier=2.0 +terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"] +terminal_bench_config.harbor.n_concurrent_trials=675 +terminal_bench_config.harbor.log_level=INFO +terminal_bench_config.harbor.enable_reward_shaping=false +terminal_bench_config.harbor.enable_error_classification=true +terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"] +terminal_bench_config.harbor.default_error_treatment=zero +terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"] +terminal_bench_config.harbor.zero_exceptions=[] +terminal_bench_config.model_info.max_input_tokens=32000 +terminal_bench_config.model_info.max_output_tokens=4096 +terminal_bench_config.archiving.enabled=false +terminal_bench_config.trace_upload.enabled=true +terminal_bench_config.trace_upload.repo_org=DCAgent +terminal_bench_config.trace_upload.episodes=last +terminal_bench_config.trace_upload.dataset_type=SFT +terminal_bench_config.trace_upload.cleanup=true +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630125.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-07 02:11:35.483 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:686 - Exporting wandb api key to ray runtime env +2026-06-07 02:11:35.483 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:705 - Exporting RAY_ADDRESS to ray runtime env +2026-06-07 02:11:35.483 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:730 - Exporting `NCCL_SOCKET_IFNAME` to ray runtime env: ib0 +2026-06-07 02:11:35.484 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:730 - Exporting `NCCL_SOCKET_FAMILY` to ray runtime env: AF_INET +2026-06-07 02:11:35.484 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:730 - Exporting `NCCL_DEBUG` to ray runtime env: WARN +2026-06-07 02:11:35,484 INFO worker.py:1680 -- Using address 10.128.32.34:6379 set in the environment variable RAY_ADDRESS +2026-06-07 02:11:35,519 INFO worker.py:1821 -- Connecting to existing Ray cluster at address: 10.128.32.34:6379... +2026-06-07 02:11:35,529 INFO worker.py:2007 -- Connected to Ray cluster. +/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0 + warnings.warn( +(raylet, ip=10.128.32.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-07 02:11:37.864 | INFO | skyrl_train.utils.ppo_utils:sync_registries:546 - Synced registries to ray actor +(skyrl_entrypoint pid=2701319) 2026-06-07 02:11:44.748 | INFO  | skyrl_train.entrypoints.main_base:_configure_log_level:212 - SkyRL log level set to: INFO +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 17x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) +(skyrl_entrypoint pid=2701319) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(skyrl_entrypoint pid=2701319) 2026-06-07 02:11:45.133 | INFO  | examples.terminal_bench.dataset:_load_data_files:40 - Loading data from: /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=2701319) 2026-06-07 02:11:48.174 | INFO  | examples.terminal_bench.dataset:_load_data_files:50 - Found 5000 valid task directories out of 5000 total directories +(skyrl_entrypoint pid=2701319) 2026-06-07 02:11:48.174 | INFO  | examples.terminal_bench.dataset:__init__:27 - TerminalBenchTaskDataset initialized with 5000 task paths +(skyrl_entrypoint pid=2701319) 2026-06-07 02:11:48.187 | INFO  | skyrl_train.entrypoints.main_base:_setup_trainer:405 - data: +(skyrl_entrypoint pid=2701319) train_data: +(skyrl_entrypoint pid=2701319) - /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=2701319) val_data: [] +(skyrl_entrypoint pid=2701319) trainer: +(skyrl_entrypoint pid=2701319) placement: +(skyrl_entrypoint pid=2701319) colocate_all: false +(skyrl_entrypoint pid=2701319) colocate_policy_ref: true +(skyrl_entrypoint pid=2701319) policy_num_nodes: 2 +(skyrl_entrypoint pid=2701319) policy_num_gpus_per_node: 4 +(skyrl_entrypoint pid=2701319) critic_num_nodes: 1 +(skyrl_entrypoint pid=2701319) critic_num_gpus_per_node: 4 +(skyrl_entrypoint pid=2701319) ref_num_nodes: 2 +(skyrl_entrypoint pid=2701319) ref_num_gpus_per_node: 4 +(skyrl_entrypoint pid=2701319) policy_strict_spread_pg: false +(skyrl_entrypoint pid=2701319) policy_per_gpu_bundles: false +(skyrl_entrypoint pid=2701319) sequence_parallel_backend: ulysses +(skyrl_entrypoint pid=2701319) strategy: fsdp2 +(skyrl_entrypoint pid=2701319) policy: +(skyrl_entrypoint pid=2701319) model: +(skyrl_entrypoint pid=2701319) path: /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=2701319) lora: +(skyrl_entrypoint pid=2701319) rank: 0 +(skyrl_entrypoint pid=2701319) alpha: 16 +(skyrl_entrypoint pid=2701319) dropout: 0 +(skyrl_entrypoint pid=2701319) lora_sync_path: /tmp/skyrl_lora_sync +(skyrl_entrypoint pid=2701319) target_modules: all-linear +(skyrl_entrypoint pid=2701319) exclude_modules: null +(skyrl_entrypoint pid=2701319) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=2701319) optimizer_config: +(skyrl_entrypoint pid=2701319) optimizer: AdamW +(skyrl_entrypoint pid=2701319) lr: 8.0e-06 +(skyrl_entrypoint pid=2701319) adam_betas: +(skyrl_entrypoint pid=2701319) - 0.9 +(skyrl_entrypoint pid=2701319) - 0.999 +(skyrl_entrypoint pid=2701319) weight_decay: 0.0 +(skyrl_entrypoint pid=2701319) max_grad_norm: 0.9 +(skyrl_entrypoint pid=2701319) offload_after_step: true +(skyrl_entrypoint pid=2701319) num_warmup_steps: 0 +(skyrl_entrypoint pid=2701319) scheduler: constant_with_warmup +(skyrl_entrypoint pid=2701319) optimizer_kwargs: {} +(skyrl_entrypoint pid=2701319) fsdp_config: +(skyrl_entrypoint pid=2701319) cpu_offload: false +(skyrl_entrypoint pid=2701319) reshard_after_forward: true +(skyrl_entrypoint pid=2701319) fsdp_size: 4 +(skyrl_entrypoint pid=2701319) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=2701319) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=2701319) moe_router_replay: false +(skyrl_entrypoint pid=2701319) moe_grouped_gemm: false +(skyrl_entrypoint pid=2701319) ep_comm_backend: torch +(skyrl_entrypoint pid=2701319) deepep_num_sms: 20 +(skyrl_entrypoint pid=2701319) deepep_token_chunk_size: null +(skyrl_entrypoint pid=2701319) sequence_parallel_size: 1 +(skyrl_entrypoint pid=2701319) use_torch_compile: false +(skyrl_entrypoint pid=2701319) record_memory: false +(skyrl_entrypoint pid=2701319) megatron_config: +(skyrl_entrypoint pid=2701319) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) context_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_tensor_parallel_size: null +(skyrl_entrypoint pid=2701319) ddp_config: +(skyrl_entrypoint pid=2701319) grad_reduce_in_fp32: true +(skyrl_entrypoint pid=2701319) overlap_grad_reduce: false +(skyrl_entrypoint pid=2701319) overlap_param_gather: false +(skyrl_entrypoint pid=2701319) average_in_collective: true +(skyrl_entrypoint pid=2701319) model_config_kwargs: {} +(skyrl_entrypoint pid=2701319) torch_profiler_config: +(skyrl_entrypoint pid=2701319) enable: false +(skyrl_entrypoint pid=2701319) ranks: [] +(skyrl_entrypoint pid=2701319) save_path: null +(skyrl_entrypoint pid=2701319) optimizer_config_kwargs: +(skyrl_entrypoint pid=2701319) overlap_cpu_optimizer_d2h_h2d: false +(skyrl_entrypoint pid=2701319) use_precision_aware_optimizer: false +(skyrl_entrypoint pid=2701319) optimizer_cpu_offload: false +(skyrl_entrypoint pid=2701319) optimizer_offload_fraction: 0.0 +(skyrl_entrypoint pid=2701319) transformer_config_kwargs: +(skyrl_entrypoint pid=2701319) recompute_granularity: full +(skyrl_entrypoint pid=2701319) recompute_modules: +(skyrl_entrypoint pid=2701319) - core_attn +(skyrl_entrypoint pid=2701319) recompute_method: uniform +(skyrl_entrypoint pid=2701319) recompute_num_layers: 1 +(skyrl_entrypoint pid=2701319) empty_cuda_cache: true +(skyrl_entrypoint pid=2701319) ref: +(skyrl_entrypoint pid=2701319) model: +(skyrl_entrypoint pid=2701319) path: ${trainer.policy.model.path} +(skyrl_entrypoint pid=2701319) sequence_parallel_size: 1 +(skyrl_entrypoint pid=2701319) deepspeed_config: ${deepspeed_config.eval} +(skyrl_entrypoint pid=2701319) fsdp_config: +(skyrl_entrypoint pid=2701319) cpu_offload: false +(skyrl_entrypoint pid=2701319) reshard_after_forward: true +(skyrl_entrypoint pid=2701319) fsdp_size: 4 +(skyrl_entrypoint pid=2701319) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=2701319) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=2701319) moe_router_replay: false +(skyrl_entrypoint pid=2701319) moe_grouped_gemm: false +(skyrl_entrypoint pid=2701319) ep_comm_backend: torch +(skyrl_entrypoint pid=2701319) deepep_num_sms: 20 +(skyrl_entrypoint pid=2701319) deepep_token_chunk_size: null +(skyrl_entrypoint pid=2701319) megatron_config: +(skyrl_entrypoint pid=2701319) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) context_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=2701319) model_config_kwargs: {} +(skyrl_entrypoint pid=2701319) transformer_config_kwargs: {} +(skyrl_entrypoint pid=2701319) critic: +(skyrl_entrypoint pid=2701319) model: +(skyrl_entrypoint pid=2701319) path: null +(skyrl_entrypoint pid=2701319) lora: +(skyrl_entrypoint pid=2701319) rank: 0 +(skyrl_entrypoint pid=2701319) alpha: 16 +(skyrl_entrypoint pid=2701319) dropout: 0 +(skyrl_entrypoint pid=2701319) target_modules: all-linear +(skyrl_entrypoint pid=2701319) exclude_modules: null +(skyrl_entrypoint pid=2701319) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=2701319) optimizer_config: +(skyrl_entrypoint pid=2701319) optimizer: AdamW +(skyrl_entrypoint pid=2701319) lr: 5.0e-06 +(skyrl_entrypoint pid=2701319) adam_betas: +(skyrl_entrypoint pid=2701319) - 0.9 +(skyrl_entrypoint pid=2701319) - 0.999 +(skyrl_entrypoint pid=2701319) weight_decay: 0.01 +(skyrl_entrypoint pid=2701319) max_grad_norm: 1.0 +(skyrl_entrypoint pid=2701319) offload_after_step: true +(skyrl_entrypoint pid=2701319) num_warmup_steps: 0 +(skyrl_entrypoint pid=2701319) scheduler: constant_with_warmup +(skyrl_entrypoint pid=2701319) optimizer_kwargs: {} +(skyrl_entrypoint pid=2701319) fsdp_config: +(skyrl_entrypoint pid=2701319) cpu_offload: false +(skyrl_entrypoint pid=2701319) reshard_after_forward: true +(skyrl_entrypoint pid=2701319) fsdp_size: -1 +(skyrl_entrypoint pid=2701319) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=2701319) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=2701319) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=2701319) moe_router_replay: false +(skyrl_entrypoint pid=2701319) moe_grouped_gemm: false +(skyrl_entrypoint pid=2701319) ep_comm_backend: torch +(skyrl_entrypoint pid=2701319) deepep_num_sms: 20 +(skyrl_entrypoint pid=2701319) deepep_token_chunk_size: null +(skyrl_entrypoint pid=2701319) sequence_parallel_size: 1 +(skyrl_entrypoint pid=2701319) algorithm: +(skyrl_entrypoint pid=2701319) advantage_estimator: rloo_n +(skyrl_entrypoint pid=2701319) kl_ctrl: +(skyrl_entrypoint pid=2701319) type: fixed +(skyrl_entrypoint pid=2701319) kl_target: 0.1 +(skyrl_entrypoint pid=2701319) horizon: 10000 +(skyrl_entrypoint pid=2701319) kl_estimator_type: k3 +(skyrl_entrypoint pid=2701319) use_kl_estimator_k3: false +(skyrl_entrypoint pid=2701319) use_abs_kl: false +(skyrl_entrypoint pid=2701319) use_kl_in_reward: false +(skyrl_entrypoint pid=2701319) use_kl_loss: false +(skyrl_entrypoint pid=2701319) kl_loss_coef: 0.0 +(skyrl_entrypoint pid=2701319) use_entropy_loss: false +(skyrl_entrypoint pid=2701319) entropy_loss_coef: 0.01 +(skyrl_entrypoint pid=2701319) advantage_batch_normalize: false +(skyrl_entrypoint pid=2701319) value_head_prefix: value_head +(skyrl_entrypoint pid=2701319) policy_loss_type: regular +(skyrl_entrypoint pid=2701319) loss_reduction: sequence_mean +(skyrl_entrypoint pid=2701319) global_loss_denom: null +(skyrl_entrypoint pid=2701319) grpo_norm_by_std: true +(skyrl_entrypoint pid=2701319) rloo_n_min_group_size: 4 +(skyrl_entrypoint pid=2701319) rloo_n_filter_zero_reward_groups: true +(skyrl_entrypoint pid=2701319) lambd: 1.0 +(skyrl_entrypoint pid=2701319) gamma: 1.0 +(skyrl_entrypoint pid=2701319) eps_clip_low: 0.2 +(skyrl_entrypoint pid=2701319) eps_clip_high: 0.05 +(skyrl_entrypoint pid=2701319) clip_ratio_c: 3.0 +(skyrl_entrypoint pid=2701319) tis_imp_ratio_cap: -1.0 +(skyrl_entrypoint pid=2701319) use_tis: false +(skyrl_entrypoint pid=2701319) sapo: +(skyrl_entrypoint pid=2701319) tau_pos: 1.0 +(skyrl_entrypoint pid=2701319) tau_neg: 1.05 +(skyrl_entrypoint pid=2701319) value_clip: 0.2 +(skyrl_entrypoint pid=2701319) dynamic_sampling: +(skyrl_entrypoint pid=2701319) type: null +(skyrl_entrypoint pid=2701319) max_sample_batches: 30 +(skyrl_entrypoint pid=2701319) min_replace_ratio: 0.3 +(skyrl_entrypoint pid=2701319) clip_cov: +(skyrl_entrypoint pid=2701319) clip_ratio: 0.0002 +(skyrl_entrypoint pid=2701319) clip_cov_lb: 1.0 +(skyrl_entrypoint pid=2701319) clip_cov_ub: 5.0 +(skyrl_entrypoint pid=2701319) kl_cov: +(skyrl_entrypoint pid=2701319) kl_cov_frac: 0.2 +(skyrl_entrypoint pid=2701319) ppo_kl_coef: 1.0 +(skyrl_entrypoint pid=2701319) cispo: +(skyrl_entrypoint pid=2701319) cispo_eps_clip_low: 0 +(skyrl_entrypoint pid=2701319) cispo_eps_clip_high: 5 +(skyrl_entrypoint pid=2701319) z_clip: +(skyrl_entrypoint pid=2701319) enabled: false +(skyrl_entrypoint pid=2701319) alpha: 0.97 +(skyrl_entrypoint pid=2701319) z_thresh: 2.5 +(skyrl_entrypoint pid=2701319) warmup_steps: 25 +(skyrl_entrypoint pid=2701319) mode: zscore +(skyrl_entrypoint pid=2701319) clip_option: adaptive_scaling +(skyrl_entrypoint pid=2701319) clip_factor: 1.0 +(skyrl_entrypoint pid=2701319) skip_update_on_spike: false +(skyrl_entrypoint pid=2701319) stale_clip: +(skyrl_entrypoint pid=2701319) enabled: false +(skyrl_entrypoint pid=2701319) alpha: 0.3 +(skyrl_entrypoint pid=2701319) entropy_threshold: 0.15 +(skyrl_entrypoint pid=2701319) entropy_window: 10 +(skyrl_entrypoint pid=2701319) min_lr_scale: 0.1 +(skyrl_entrypoint pid=2701319) max_seq_len: 1004095 +(skyrl_entrypoint pid=2701319) fully_async: +(skyrl_entrypoint pid=2701319) max_staleness_steps: 16 +(skyrl_entrypoint pid=2701319) num_parallel_generation_workers: 338 +(skyrl_entrypoint pid=2701319) gradient_checkpointing: true +(skyrl_entrypoint pid=2701319) gradient_checkpointing_use_reentrant: false +(skyrl_entrypoint pid=2701319) seed: 42 +(skyrl_entrypoint pid=2701319) resume_mode: latest +(skyrl_entrypoint pid=2701319) resume_path: null +(skyrl_entrypoint pid=2701319) ckpt_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints +(skyrl_entrypoint pid=2701319) max_ckpts_to_keep: -1 +(skyrl_entrypoint pid=2701319) ckpt_interval: 2 +(skyrl_entrypoint pid=2701319) hf_save_interval: 5 +(skyrl_entrypoint pid=2701319) hf_upload_mode: latest +(skyrl_entrypoint pid=2701319) export_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports +(skyrl_entrypoint pid=2701319) bf16: true +(skyrl_entrypoint pid=2701319) epochs: 2 +(skyrl_entrypoint pid=2701319) max_steps: 80 +(skyrl_entrypoint pid=2701319) update_epochs_per_batch: 1 +(skyrl_entrypoint pid=2701319) train_batch_size: 64 +(skyrl_entrypoint pid=2701319) policy_mini_batch_size: 64 +(skyrl_entrypoint pid=2701319) critic_mini_batch_size: 256 +(skyrl_entrypoint pid=2701319) micro_train_batch_size_per_gpu: 1 +(skyrl_entrypoint pid=2701319) micro_forward_batch_size_per_gpu: 4 +(skyrl_entrypoint pid=2701319) update_ref_every_epoch: false +(skyrl_entrypoint pid=2701319) use_sample_packing: true +(skyrl_entrypoint pid=2701319) eval_batch_size: 64 +(skyrl_entrypoint pid=2701319) eval_before_train: false +(skyrl_entrypoint pid=2701319) eval_interval: 999999 +(skyrl_entrypoint pid=2701319) max_prompt_length: 999999 +(skyrl_entrypoint pid=2701319) flash_attn: true +(skyrl_entrypoint pid=2701319) disable_fast_tokenizer: false +(skyrl_entrypoint pid=2701319) target_modules: null +(skyrl_entrypoint pid=2701319) exclude_modules: null +(skyrl_entrypoint pid=2701319) project_name: OpenThoughts-Agent +(skyrl_entrypoint pid=2701319) run_name: ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=2701319) logger: console +(skyrl_entrypoint pid=2701319) tracker_commit_each_step: true +(skyrl_entrypoint pid=2701319) dump_data_batch: false +(skyrl_entrypoint pid=2701319) dump_eval_results: true +(skyrl_entrypoint pid=2701319) log_level: INFO +(skyrl_entrypoint pid=2701319) rope_scaling: null +(skyrl_entrypoint pid=2701319) rope_theta: null +(skyrl_entrypoint pid=2701319) step_wise_training: false +(skyrl_entrypoint pid=2701319) hf_hub_repo_id: laion/ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=2701319) hf_hub_private: false +(skyrl_entrypoint pid=2701319) hf_hub_revision: main +(skyrl_entrypoint pid=2701319) enable_db_registration: false +(skyrl_entrypoint pid=2701319) generator: +(skyrl_entrypoint pid=2701319) model_name: ${trainer.policy.model.path} +(skyrl_entrypoint pid=2701319) model_dtype: bfloat16 +(skyrl_entrypoint pid=2701319) timeout_multiplier: 1.0 +(skyrl_entrypoint pid=2701319) run_engines_locally: true +(skyrl_entrypoint pid=2701319) num_inference_engines: 48 +(skyrl_entrypoint pid=2701319) backend: vllm +(skyrl_entrypoint pid=2701319) weight_sync_backend: nccl +(skyrl_entrypoint pid=2701319) fuse_weights: false +(skyrl_entrypoint pid=2701319) weight_transfer_threshold_cuda_ipc_GB: 1.0 +(skyrl_entrypoint pid=2701319) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=2701319) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=2701319) inference_engine_expert_parallel_size: 1 +(skyrl_entrypoint pid=2701319) inference_engine_data_parallel_size: 1 +(skyrl_entrypoint pid=2701319) n_samples_per_prompt: 8 +(skyrl_entrypoint pid=2701319) async_engine: true +(skyrl_entrypoint pid=2701319) batched: false +(skyrl_entrypoint pid=2701319) max_input_length: ${trainer.max_prompt_length} +(skyrl_entrypoint pid=2701319) vllm_v1_disable_multiproc: true +(skyrl_entrypoint pid=2701319) enable_prefix_caching: true +(skyrl_entrypoint pid=2701319) enable_chunked_prefill: true +(skyrl_entrypoint pid=2701319) max_num_batched_tokens: 65536 +(skyrl_entrypoint pid=2701319) enforce_eager: true +(skyrl_entrypoint pid=2701319) fully_sharded_loras: false +(skyrl_entrypoint pid=2701319) enable_ray_prometheus_stats: false +(skyrl_entrypoint pid=2701319) vllm_stats_interval: 1 +(skyrl_entrypoint pid=2701319) gpu_memory_utilization: 0.75 +(skyrl_entrypoint pid=2701319) max_num_seqs: 24 +(skyrl_entrypoint pid=2701319) remote_inference_engine_urls: +(skyrl_entrypoint pid=2701319) - 127.0.0.1:8001 +(skyrl_entrypoint pid=2701319) enable_http_endpoint: true +(skyrl_entrypoint pid=2701319) http_endpoint_host: 127.0.0.1 +(skyrl_entrypoint pid=2701319) http_endpoint_port: 8000 +(skyrl_entrypoint pid=2701319) max_turns: 999999 +(skyrl_entrypoint pid=2701319) chat_template: +(skyrl_entrypoint pid=2701319) source: name +(skyrl_entrypoint pid=2701319) name_or_path: null +(skyrl_entrypoint pid=2701319) chat_template_kwargs: {} +(skyrl_entrypoint pid=2701319) engine_init_kwargs: +(skyrl_entrypoint pid=2701319) max_model_len: 32768 +(skyrl_entrypoint pid=2701319) custom_chat_template_chat_completion_path: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=2701319) served_model_name: 0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=2701319) override_existing_update_group: disable +(skyrl_entrypoint pid=2701319) sampling_params: +(skyrl_entrypoint pid=2701319) max_generate_length: 4096 +(skyrl_entrypoint pid=2701319) repetition_penalty: 1.0 +(skyrl_entrypoint pid=2701319) temperature: 0.7 +(skyrl_entrypoint pid=2701319) top_p: 0.95 +(skyrl_entrypoint pid=2701319) min_p: 0.0 +(skyrl_entrypoint pid=2701319) top_k: 20 +(skyrl_entrypoint pid=2701319) logprobs: null +(skyrl_entrypoint pid=2701319) stop: null +(skyrl_entrypoint pid=2701319) use_conversation_multi_turn: true +(skyrl_entrypoint pid=2701319) append_eos_token_after_stop_str_in_multi_turn: true +(skyrl_entrypoint pid=2701319) eval_sampling_params: +(skyrl_entrypoint pid=2701319) max_generate_length: ${generator.sampling_params.max_generate_length} +(skyrl_entrypoint pid=2701319) repetition_penalty: 1.0 +(skyrl_entrypoint pid=2701319) temperature: 0.0 +(skyrl_entrypoint pid=2701319) top_p: 1.0 +(skyrl_entrypoint pid=2701319) min_p: 0.0 +(skyrl_entrypoint pid=2701319) top_k: -1 +(skyrl_entrypoint pid=2701319) logprobs: null +(skyrl_entrypoint pid=2701319) stop: null +(skyrl_entrypoint pid=2701319) eval_n_samples_per_prompt: 8 +(skyrl_entrypoint pid=2701319) zero_reward_on_non_stop: false +(skyrl_entrypoint pid=2701319) apply_overlong_filtering: false +(skyrl_entrypoint pid=2701319) rope_scaling: ${trainer.rope_scaling} +(skyrl_entrypoint pid=2701319) rope_theta: ${trainer.rope_theta} +(skyrl_entrypoint pid=2701319) teacher: +(skyrl_entrypoint pid=2701319) model_path: null +(skyrl_entrypoint pid=2701319) top_k_logprobs: 256 +(skyrl_entrypoint pid=2701319) num_inference_engines: 1 +(skyrl_entrypoint pid=2701319) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=2701319) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=2701319) gpu_memory_utilization: 0.9 +(skyrl_entrypoint pid=2701319) enforce_eager: false +(skyrl_entrypoint pid=2701319) backend: vllm +(skyrl_entrypoint pid=2701319) engine_init_kwargs: {} +(skyrl_entrypoint pid=2701319) environment: +(skyrl_entrypoint pid=2701319) env_class: gsm8k +(skyrl_entrypoint pid=2701319) skyrl_gym: +(skyrl_entrypoint pid=2701319) max_env_workers: 32 +(skyrl_entrypoint pid=2701319) text2sql: +(skyrl_entrypoint pid=2701319) db_path: /home/ray/default/sql_data +(skyrl_entrypoint pid=2701319) llm_as_a_judge: +(skyrl_entrypoint pid=2701319) model: gpt-4o-mini +(skyrl_entrypoint pid=2701319) base_url: null +(skyrl_entrypoint pid=2701319) search: +(skyrl_entrypoint pid=2701319) log_requests: false +(skyrl_entrypoint pid=2701319) search_url: http://127.0.0.1:8000/retrieve +(skyrl_entrypoint pid=2701319) topk: 3 +(skyrl_entrypoint pid=2701319) timeout: 30 +(skyrl_entrypoint pid=2701319) rollout: +(skyrl_entrypoint pid=2701319) fanout: +(skyrl_entrypoint pid=2701319) enabled: true +(skyrl_entrypoint pid=2701319) num_coordinators: 4 +(skyrl_entrypoint pid=2701319) cpus_per_coordinator: 8 +(skyrl_entrypoint pid=2701319) deepspeed_config: +(skyrl_entrypoint pid=2701319) train: +(skyrl_entrypoint pid=2701319) zero_optimization: +(skyrl_entrypoint pid=2701319) stage: 3 +(skyrl_entrypoint pid=2701319) offload_param: +(skyrl_entrypoint pid=2701319) device: none +(skyrl_entrypoint pid=2701319) offload_optimizer: +(skyrl_entrypoint pid=2701319) device: none +(skyrl_entrypoint pid=2701319) pin_memory: true +(skyrl_entrypoint pid=2701319) sub_group_size: auto +(skyrl_entrypoint pid=2701319) reduce_bucket_size: auto +(skyrl_entrypoint pid=2701319) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=2701319) stage3_prefetch_bucket_size: auto +(skyrl_entrypoint pid=2701319) stage3_max_live_parameters: auto +(skyrl_entrypoint pid=2701319) stage3_max_reuse_distance: auto +(skyrl_entrypoint pid=2701319) round_robin_gradients: true +(skyrl_entrypoint pid=2701319) zero_hpz_partition_size: 1 +(skyrl_entrypoint pid=2701319) zero_quantized_weights: false +(skyrl_entrypoint pid=2701319) zero_quantized_gradients: false +(skyrl_entrypoint pid=2701319) torch_autocast: +(skyrl_entrypoint pid=2701319) enabled: true +(skyrl_entrypoint pid=2701319) dtype: bfloat16 +(skyrl_entrypoint pid=2701319) disable_trace_cache: false +(skyrl_entrypoint pid=2701319) data_types: +(skyrl_entrypoint pid=2701319) grad_accum_dtype: fp32 +(skyrl_entrypoint pid=2701319) gradient_clipping: 1.0 +(skyrl_entrypoint pid=2701319) wall_clock_breakdown: false +(skyrl_entrypoint pid=2701319) prescale_gradient: false +(skyrl_entrypoint pid=2701319) eval: +(skyrl_entrypoint pid=2701319) zero_optimization: +(skyrl_entrypoint pid=2701319) stage: 3 +(skyrl_entrypoint pid=2701319) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=2701319) offload_param: +(skyrl_entrypoint pid=2701319) device: cpu +(skyrl_entrypoint pid=2701319) pin_memory: true +(skyrl_entrypoint pid=2701319) torch_autocast: +(skyrl_entrypoint pid=2701319) enabled: true +(skyrl_entrypoint pid=2701319) dtype: bfloat16 +(skyrl_entrypoint pid=2701319) gradient_clipping: 1.0 +(skyrl_entrypoint pid=2701319) prescale_gradient: false +(skyrl_entrypoint pid=2701319) wall_clock_breakdown: false +(skyrl_entrypoint pid=2701319) terminal_bench_config: +(skyrl_entrypoint pid=2701319) trials_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs +(skyrl_entrypoint pid=2701319) harbor: +(skyrl_entrypoint pid=2701319) name: terminus-2 +(skyrl_entrypoint pid=2701319) max_episodes: 999999 +(skyrl_entrypoint pid=2701319) enable_summarize: false +(skyrl_entrypoint pid=2701319) store_all_messages: true +(skyrl_entrypoint pid=2701319) trajectory_config: +(skyrl_entrypoint pid=2701319) raw_content: true +(skyrl_entrypoint pid=2701319) enable_episode_logging: false +(skyrl_entrypoint pid=2701319) record_terminal_session: false +(skyrl_entrypoint pid=2701319) enable_pane_logging: false +(skyrl_entrypoint pid=2701319) strict_json_parser: true +(skyrl_entrypoint pid=2701319) interleaved_thinking: true +(skyrl_entrypoint pid=2701319) extra_body: +(skyrl_entrypoint pid=2701319) chat_template_kwargs: +(skyrl_entrypoint pid=2701319) enable_thinking: true +(skyrl_entrypoint pid=2701319) override_timeout_sec: 900 +(skyrl_entrypoint pid=2701319) override_cpus: 1 +(skyrl_entrypoint pid=2701319) override_memory_mb: 2048 +(skyrl_entrypoint pid=2701319) override_storage_mb: 2048 +(skyrl_entrypoint pid=2701319) auto_snapshot: true +(skyrl_entrypoint pid=2701319) verifier_override_timeout_sec: 120 +(skyrl_entrypoint pid=2701319) max_retries: 3 +(skyrl_entrypoint pid=2701319) min_wait_sec: 60.0 +(skyrl_entrypoint pid=2701319) max_wait_sec: 600.0 +(skyrl_entrypoint pid=2701319) wait_multiplier: 2.0 +(skyrl_entrypoint pid=2701319) exclude_exceptions: +(skyrl_entrypoint pid=2701319) - VerifierTimeoutError +(skyrl_entrypoint pid=2701319) - VerifierRuntimeError +(skyrl_entrypoint pid=2701319) - RewardFileNotFoundError +(skyrl_entrypoint pid=2701319) - RewardFileEmptyError +(skyrl_entrypoint pid=2701319) - VerifierOutputParseError +(skyrl_entrypoint pid=2701319) n_concurrent_trials: 675 +(skyrl_entrypoint pid=2701319) log_level: INFO +(skyrl_entrypoint pid=2701319) enable_reward_shaping: false +(skyrl_entrypoint pid=2701319) enable_error_classification: true +(skyrl_entrypoint pid=2701319) mask_exceptions: +(skyrl_entrypoint pid=2701319) - DaytonaError +(skyrl_entrypoint pid=2701319) - EnvironmentStartTimeoutError +(skyrl_entrypoint pid=2701319) - NetworkError +(skyrl_entrypoint pid=2701319) - ConnectionError +(skyrl_entrypoint pid=2701319) - RewardFileNotFoundError +(skyrl_entrypoint pid=2701319) - RewardFileEmptyError +(skyrl_entrypoint pid=2701319) - AgentEnvironmentTimeoutError +(skyrl_entrypoint pid=2701319) - ContextLengthExceededError +(skyrl_entrypoint pid=2701319) default_error_treatment: zero +(skyrl_entrypoint pid=2701319) passthrough_exceptions: +(skyrl_entrypoint pid=2701319) - AgentTimeoutError +(skyrl_entrypoint pid=2701319) zero_exceptions: [] +(skyrl_entrypoint pid=2701319) model_info: +(skyrl_entrypoint pid=2701319) max_input_tokens: 32000 +(skyrl_entrypoint pid=2701319) max_output_tokens: 4096 +(skyrl_entrypoint pid=2701319) archiving: +(skyrl_entrypoint pid=2701319) enabled: false +(skyrl_entrypoint pid=2701319) trace_upload: +(skyrl_entrypoint pid=2701319) enabled: true +(skyrl_entrypoint pid=2701319) repo_org: DCAgent +(skyrl_entrypoint pid=2701319) episodes: last +(skyrl_entrypoint pid=2701319) dataset_type: SFT +(skyrl_entrypoint pid=2701319) cleanup: true +(skyrl_entrypoint pid=2701319)  +(skyrl_entrypoint pid=2701319) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(skyrl_entrypoint pid=2701319) No module named 'vllm._version' +(skyrl_entrypoint pid=2701319) from .version import __version__, __version_tuple__ # isort:skip +(skyrl_entrypoint pid=2701319) W0607 02:12:04.505000 2701319 envs/rl/lib/python3.12/site-packages/torch/utils/cpp_extension.py:117] No CUDA runtime is found, using CUDA_HOME='/e/software/default/stages/2026/software/CUDA/13' +[2026-06-07 02:12:05,742 E 2700889 2701293] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RegistryActor pid=2575022, ip=10.128.32.41) [2026-06-07 02:12:06,659 E 2575022 2575062] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(skyrl_entrypoint pid=2701319) [2026-06-07 02:12:08,855 E 2701319 2701362] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 2x across cluster] +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 31x across cluster] +(pid=2666524, ip=10.128.32.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=2666524, ip=10.128.32.38) No module named 'vllm._version' +(pid=2666524, ip=10.128.32.38) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.32.48) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 64x across cluster] +(pid=2539107, ip=10.128.32.40) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 7x across cluster] +(pid=2539107, ip=10.128.32.40) No module named 'vllm._version' [repeated 7x across cluster] +(pid=2539107, ip=10.128.32.40) from .version import __version__, __version_tuple__ # isort:skip [repeated 7x across cluster] +(raylet, ip=10.128.32.47) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 116x across cluster] +(pid=2593412, ip=10.128.32.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 16x across cluster] +(pid=2593412, ip=10.128.32.36) No module named 'vllm._version' [repeated 16x across cluster] +(pid=2593412, ip=10.128.32.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:28.309 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:28.334 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:28.335 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:28.982 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:29.008 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:29.008 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:29.008 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:29.008 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) 2026-06-07 02:12:29.059 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 1.94s (attempt 1/5) to avoid port collisions +(raylet, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 106x across cluster] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(pid=2659898, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 13x across cluster] +(pid=2659898, ip=10.128.32.43) No module named 'vllm._version' [repeated 13x across cluster] +(pid=2659898, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:31.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:31.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:31.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(skyrl_entrypoint pid=2701319) [2026-06-07 02:12:33] INFO inference_engine_client_http_endpoint.py:350: Starting server on 0.0.0.0:8000 +(skyrl_entrypoint pid=2701319) [2026-06-07 02:12:34] INFO inference_engine_client_http_endpoint.py:242: Starting inference HTTP endpoint... +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:32.760 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:32.763 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:32.763 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:32.763 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:32.763 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) 2026-06-07 02:12:32.784 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 1.51s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(skyrl_entrypoint pid=2701319) [2026-06-07 02:12:34] INFO inference_engine_client_http_endpoint.py:229: Server ready after 2 attempts (2 seconds) +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.803 | INFO  | skyrl_train.inference_engines.inference_engine_client:_spin_up_http_endpoint:969 - InferenceEngineClient HTTP endpoint started on 127.0.0.1:8000 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.803 | INFO  | skyrl_train.inference_engines.inference_engine_client:__init__:61 - InferenceEngineClient initialized with 48 engines. +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.805 | INFO  | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.806 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 675. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.817 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.818 | INFO  | skyrl_train.utils.trainer_utils:build_dataloader:656 - Total steps: 156 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.818 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:357 - Length of train_dataloader: 5000 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.818 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:358 - Number of steps per epoch: 78 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:34.818 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:359 - Total training steps: 80 +(raylet, ip=10.128.32.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 63x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(pid=2955859, ip=10.128.32.39) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 15x across cluster] +(pid=2955859, ip=10.128.32.39) No module named 'vllm._version' [repeated 15x across cluster] +(pid=2955859, ip=10.128.32.39) from .version import __version__, __version_tuple__ # isort:skip [repeated 15x across cluster] +(pid=2623829, ip=10.128.32.46) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:39.814 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=1, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:39.815 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:39.815 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) 2026-06-07 02:12:38.211 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) 2026-06-07 02:12:38.234 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) 2026-06-07 02:12:38.234 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) 2026-06-07 02:12:38.234 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) 2026-06-07 02:12:38.234 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) 2026-06-07 02:12:38.253 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.33s (attempt 1/5) to avoid port collisions +(bundle_reservation_check_func pid=2701396) [2026-06-07 02:12:39,951 E 2701396 2701436] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:40.760 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 72-143 (NUMA node 1) for GPU 1 +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:40.764 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 1 +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:40.764 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:40.765 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:40.765 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) 2026-06-07 02:12:40.793 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.14s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=3076043, ip=10.128.32.42) 2026-06-07 02:12:45.362 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076043, ip=10.128.32.42) 2026-06-07 02:12:45.364 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076043, ip=10.128.32.42) 2026-06-07 02:12:45.364 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) [2026-06-07 02:12:41,839 E 2666524 2666626] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) 2026-06-07 02:12:45.860 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) 2026-06-07 02:12:45.883 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) 2026-06-07 02:12:45.883 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) 2026-06-07 02:12:45.883 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) 2026-06-07 02:12:45.883 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) 2026-06-07 02:12:45.916 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.38s (attempt 1/5) to avoid port collisions [repeated 4x across cluster] +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) 2026-06-07 02:12:45 INFO [ipv4-debug] hostname=jpbo-041-46.jupiter.internal +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) 2026-06-07 02:12:45 INFO [ipv4-debug] _global_node.node_ip_address=10.128.32.46 +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) 2026-06-07 02:12:45 INFO [ipv4-debug] get_node_ip_address()=10.128.32.46 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:12:46.122 | INFO  | skyrl_train.workers.worker:_initiate_actors:563 - Initializing process group for RayActorGroup +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [W607 02:12:46.969120626 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-46.jupiter.internal]:46781 (errno: 97 - Address family not supported by protocol). +(raylet, ip=10.128.32.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2793258, ip=10.128.32.45) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2557696, ip=10.128.32.48) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2557696, ip=10.128.32.48) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2557696, ip=10.128.32.48) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2557696, ip=10.128.32.48) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(pid=2623903, ip=10.128.32.46) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) (EngineCore_DP0 pid=2666891) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) (EngineCore_DP0 pid=2666891) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2666657, ip=10.128.32.38) (EngineCore_DP0 pid=2666896) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) (EngineCore_DP0 pid=2666900) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) 2026-06-07 02:12:49.608 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=1, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) 2026-06-07 02:12:49.610 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) 2026-06-07 02:12:49.610 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2557696, ip=10.128.32.48) [2026-06-07 02:12:49,438 E 2557696 2557862] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) [W607 02:12:50.707628733 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) [rank0]:[W607 02:12:50.713854317 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) 2026-06-07 02:12:50.870 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) 2026-06-07 02:12:50.889 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) 2026-06-07 02:12:50.889 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) 2026-06-07 02:12:50.889 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) 2026-06-07 02:12:50.889 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) 2026-06-07 02:12:50.920 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.99s (attempt 1/5) to avoid port collisions [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) [W607 02:12:50.703217538 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-38-interconnect-1.jupiter.internal]:38761 (errno: 97 - Address family not supported by protocol). [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 107x across cluster] +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2955860, ip=10.128.32.39) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 71x across cluster] +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) (EngineCore_DP0 pid=2660131) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) No module named 'vllm._version' [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) from .version import __version__, __version_tuple__ # isort:skip [repeated 35x across cluster] +(pid=2701488) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) (EngineCore_DP0 pid=2660139) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00, std::allocator >) + 0xb0 (0x4000730cc700 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libc10.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #1: + 0x5f29220 (0x400053269220 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #2: + 0x5f4326c (0x40005328326c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #3: + 0x5f49074 (0x400053289074 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #4: + 0x5f49138 (0x400053289138 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #5: + 0x5f2ccc4 (0x40005326ccc4 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #6: c10d::TCPStore::TCPStore(std::__cxx11::basic_string, std::allocator >, c10d::TCPStoreOptions const&) + 0x104 (0x400053271564 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #7: + 0x109a094 (0x40004d01a094 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #8: + 0x113236c (0x40004d0b236c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #9: + 0x5d6d60 (0x40004c556d60 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #10: + 0x1b7a38 (0xaaaabc307a38 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #11: _PyObject_MakeTpCall + 0x98 (0xaaaabc2b5db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #12: + 0x169f50 (0xaaaabc2b9f50 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #13: + 0x1682e4 (0xaaaabc2b82e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #14: + 0x1e0ce8 (0xaaaabc330ce8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #15: + 0x1d7ddc (0xaaaabc327ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #16: + 0x646b0c (0x40004c5c6b0c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #17: _PyObject_MakeTpCall + 0x98 (0xaaaabc2b5db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #18: _PyEval_EvalFrameDefault + 0x280c (0xaaaabc3bae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #19: + 0x1808c0 (0xaaaabc2d08c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #20: + 0x182bf8 (0xaaaabc2d2bf8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #21: + 0x25fd30 (0xaaaabc3afd30 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #22: + 0x1b7d20 (0xaaaabc307d20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #23: PyObject_Vectorcall + 0x54 (0xaaaabc2b60e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #24: _PyEval_EvalFrameDefault + 0x280c (0xaaaabc3bae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #25: _PyObject_FastCallDictTstate + 0x80 (0xaaaabc2b7f20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #26: _PyObject_Call_Prepend + 0x140 (0xaaaabc2b81ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #27: + 0x1e0d80 (0xaaaabc330d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #28: + 0x1d7ddc (0xaaaabc327ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #29: _PyObject_MakeTpCall + 0x98 (0xaaaabc2b5db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #30: _PyEval_EvalFrameDefault + 0x280c (0xaaaabc3bae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #31: _PyObject_FastCallDictTstate + 0x10c (0xaaaabc2b7fac in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #32: _PyObject_Call_Prepend + 0x140 (0xaaaabc2b81ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #33: + 0x1e0d80 (0xaaaabc330d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #34: + 0x1d7ddc (0xaaaabc327ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #35: _PyObject_Call + 0x68 (0xaaaabc2b8488 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #36: _PyEval_EvalFrameDefault + 0x52ac (0xaaaabc3bd8f4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #37: PyEval_EvalCode + 0xb4 (0xaaaabc3c2eb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #38: + 0x2ccdcc (0xaaaabc41cdcc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #39: + 0x2ccef4 (0xaaaabc41cef4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #40: PyRun_StringFlags + 0x90 (0xaaaabc421050 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #41: PyRun_SimpleStringFlags + 0x58 (0xaaaabc4210f8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #42: Py_RunMain + 0x2c8 (0xaaaabc449190 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #43: Py_BytesMain + 0x64 (0xaaaabc449fb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #44: + 0x27540 (0x40003b5c7540 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #45: __libc_start_main + 0x98 (0x40003b5c7618 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) frame #46: + 0x10e0c0 (0xaaaabc25e0c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) [pynccl] dumped 0 entries to /e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace_pynccl_pid2593652 (reason=atexit) +(FSDPPolicyWorkerBase pid=2701488) `torch_dtype` is deprecated! Use `dtype` instead! [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=2623904, ip=10.128.32.46) Loading checkpoint shards: 0%| | 0/4 [00:00) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) return self.__get_result() +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) raise self._exception +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) super().__init__(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) self.llm = self._create_engine(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) return cls( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) self.engine_core = EngineCoreClient.make_async_mp_client( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) return AsyncMPClient(*client_args) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) super().__init__( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) with launch_core_engines(vllm_config, executor_class, log_stats) as ( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) next(self.gen) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) wait_for_engine_startup( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) raise RuntimeError( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {} +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) [pynccl] dumped 0 entries to /e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace_pynccl_pid2593284 (reason=atexit) +(AsyncVLLMInferenceEngine pid=984183, ip=10.128.32.37) (EngineCore_DP0 pid=984423) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3076043, ip=10.128.32.42) (EngineCore_DP0 pid=3076412) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=290611, ip=10.128.32.47) (EngineCore_DP0 pid=290849) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 10.128.32.34 (routable head IP) for coordinator litellm base_url connectivity +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:14.388 | INFO  | examples.terminal_bench.rollout_coordinator:__init__:405 - [RolloutDispatcher] configured num_coordinators=4, cpus_per_coordinator=8 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:14.409 | INFO  | examples.terminal_bench.rollout_coordinator:startup:434 - [RolloutDispatcher] PlacementGroup ready: 4 bundles x 8 CPU (SPREAD) +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:03<00:10, 3.57s/it] [repeated 47x across cluster] +(raylet, ip=10.128.32.39) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2593411, ip=10.128.32.36) (EngineCore_DP0 pid=2593642) +(AsyncVLLMInferenceEngine pid=2593413, ip=10.128.32.36) (EngineCore_DP0 pid=2593666) +(AsyncVLLMInferenceEngine pid=2593412, ip=10.128.32.36) (EngineCore_DP0 pid=2593646) +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 11x across cluster] +(FSDPPolicyWorkerBase pid=2623904, ip=10.128.32.46) [rank3]:[W607 02:13:12.044597514 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 8x across cluster] +(FSDPPolicyWorkerBase pid=2623903, ip=10.128.32.46) [2026-06-07 02:13:16,931 E 2623903 2624043] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(AsyncVLLMInferenceEngine pid=290484, ip=10.128.32.47) (EngineCore_DP0 pid=290850) +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) 2026-06-07 02:13:16.277 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=290611, ip=10.128.32.47) (EngineCore_DP0 pid=290849) +(AsyncVLLMInferenceEngine pid=290612, ip=10.128.32.47) (EngineCore_DP0 pid=290842) +(AsyncVLLMInferenceEngine pid=290737, ip=10.128.32.47) (EngineCore_DP0 pid=290838) +(AsyncVLLMInferenceEngine pid=2557696, ip=10.128.32.48) (EngineCore_DP0 pid=2558131) +(AsyncVLLMInferenceEngine pid=2557898, ip=10.128.32.48) (EngineCore_DP0 pid=2558139) +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) (EngineCore_DP0 pid=2558151) +(AsyncVLLMInferenceEngine pid=2557899, ip=10.128.32.48) (EngineCore_DP0 pid=2558147) +(AsyncVLLMInferenceEngine pid=2793126, ip=10.128.32.45) (EngineCore_DP0 pid=2793507) +(AsyncVLLMInferenceEngine pid=2793257, ip=10.128.32.45) (EngineCore_DP0 pid=2793503) +(AsyncVLLMInferenceEngine pid=2793258, ip=10.128.32.45) (EngineCore_DP0 pid=2793487) +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) +(AsyncVLLMInferenceEngine pid=2972527, ip=10.128.32.44) (EngineCore_DP0 pid=2972899) +(AsyncVLLMInferenceEngine pid=2972527, ip=10.128.32.44) (EngineCore_DP0 pid=2972899) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.30s/it] [repeated 79x across cluster] +(raylet, ip=10.128.32.39) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2972655, ip=10.128.32.44) (EngineCore_DP0 pid=2972907) +(AsyncVLLMInferenceEngine pid=2972656, ip=10.128.32.44) (EngineCore_DP0 pid=2972886) +(AsyncVLLMInferenceEngine pid=2972657, ip=10.128.32.44) (EngineCore_DP0 pid=2972891) +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.159 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(AsyncVLLMInferenceEngine pid=2538980, ip=10.128.32.40) (EngineCore_DP0 pid=2539337) +(AsyncVLLMInferenceEngine pid=2539109, ip=10.128.32.40) (EngineCore_DP0 pid=2539345) +(AsyncVLLMInferenceEngine pid=2539108, ip=10.128.32.40) (EngineCore_DP0 pid=2539361) +(AsyncVLLMInferenceEngine pid=2539107, ip=10.128.32.40) (EngineCore_DP0 pid=2539341) +(AsyncVLLMInferenceEngine pid=3076043, ip=10.128.32.42) (EngineCore_DP0 pid=3076412) +(AsyncVLLMInferenceEngine pid=3076173, ip=10.128.32.42) (EngineCore_DP0 pid=3076402) +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) (EngineCore_DP0 pid=3076408) +(AsyncVLLMInferenceEngine pid=3076172, ip=10.128.32.42) (EngineCore_DP0 pid=3076398) +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.385 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.385 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.387 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.387 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 0/4] constructed (http=10.128.32.34:8000) +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.390 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.390 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:13:20.390 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 0] startup complete +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:20.391 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 1/4 started +(AsyncVLLMInferenceEngine pid=984055, ip=10.128.32.37) (EngineCore_DP0 pid=984427) +(AsyncVLLMInferenceEngine pid=984182, ip=10.128.32.37) (EngineCore_DP0 pid=984418) +(AsyncVLLMInferenceEngine pid=984183, ip=10.128.32.37) (EngineCore_DP0 pid=984423) +(AsyncVLLMInferenceEngine pid=984310, ip=10.128.32.37) (EngineCore_DP0 pid=984419) +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) (EngineCore_DP0 pid=2956113) +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) (EngineCore_DP0 pid=2558151) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 16x across cluster] +(FSDPPolicyWorkerBase pid=2701488) [2026-06-07 02:13:17,107 E 2701488 2701649] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2955860, ip=10.128.32.39) (EngineCore_DP0 pid=2956089) +(AsyncVLLMInferenceEngine pid=2955861, ip=10.128.32.39) (EngineCore_DP0 pid=2956093) +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) +(AsyncVLLMInferenceEngine pid=2793126, ip=10.128.32.45) 2026-06-07 02:13:21.517 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.30s/it] [repeated 46x across cluster] +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.158 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) 2026-06-07 02:13:26.137 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 20x across cluster] +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.376 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.376 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.376 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.376 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 1/4] constructed (http=10.128.32.34:8000) +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.379 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.379 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=984614, ip=10.128.32.37) 2026-06-07 02:13:28.379 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 1] startup complete +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:28.381 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 2/4 started +(raylet, ip=10.128.32.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.536 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(RolloutCoordinator pid=984614, ip=10.128.32.37) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(raylet, ip=10.128.32.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 4x across cluster] +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:36.763 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 3/4 started +(RolloutCoordinator pid=2660382, ip=10.128.32.43) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.758 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.758 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.758 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.758 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 2/4] constructed (http=10.128.32.34:8000) +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.761 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.761 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=2660382, ip=10.128.32.43) 2026-06-07 02:13:36.761 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 2] startup complete +(RolloutCoordinator pid=2956288, ip=10.128.32.39) [2026-06-07 02:13:45,184 E 2956288 2956328] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.382 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(RolloutCoordinator pid=2593907, ip=10.128.32.36) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.604 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.604 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.604 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.604 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 3/4] constructed (http=10.128.32.34:8000) +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.607 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.607 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=2593907, ip=10.128.32.36) 2026-06-07 02:13:45.607 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 3] startup complete +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.608 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 4/4 started +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.608 | INFO  | examples.terminal_bench.rollout_coordinator:startup:477 - [RolloutDispatcher] 4 coordinators started +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.608 | INFO  | skyrl_train.fully_async_trainer:train:485 - Generator startup complete +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.608 | INFO  | skyrl_train.fully_async_trainer:_train_loop:510 - Started: 'load_checkpoints' +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.617 | INFO  | skyrl_train.trainer:load_checkpoints:1664 - Loading checkpoint from: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.617 | INFO  | skyrl_train.trainer:load_checkpoints:1670 - Resuming from global_step: 28 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.646 | INFO  | skyrl_train.trainer:load_checkpoints:1686 - Successfully loaded trainer state +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.660 | INFO  | skyrl_train.trainer:load_checkpoints:1696 - Successfully loaded dataloader state +(skyrl_entrypoint pid=2701319) 2026-06-07 02:13:45.660 | INFO  | skyrl_train.trainer:load_checkpoints:1705 - Loading policy checkpoint from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy +(RolloutCoordinator pid=984614, ip=10.128.32.37) [2026-06-07 02:13:53,284 E 984614 984654] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RolloutCoordinator pid=2660382, ip=10.128.32.43) [2026-06-07 02:14:01,250 E 2660382 2660422] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4876: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning. +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) warnings.warn( # warn only once +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:03.113 | INFO  | skyrl_train.trainer:load_checkpoints:1715 - Successfully loaded policy checkpoint +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:03.114 | INFO  | skyrl_train.trainer:load_checkpoints:1731 - Successfully loaded complete checkpoint state from global_step_28 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:03.114 | INFO  | skyrl_train.fully_async_trainer:_train_loop:512 - Resumed training from global_step 28 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:03.131 | INFO  | skyrl_train.utils.data_tracker:load_state:97 - Loaded data tracker state: epoch=0, consumed_in_epoch=1792, total_consumed=1792 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:03.132 | INFO  | skyrl_train.fully_async_trainer:_train_loop:510 - Finished: 'load_checkpoints', time cost: 17.52s +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:03.132 | INFO  | skyrl_train.fully_async_trainer:_train_loop:544 - Started: 'init_weight_sync_state' +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) No module named 'vllm._version' +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) from .version import __version__, __version_tuple__ # isort:skip +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) 2026-06-07 02:14:08.866 | INFO  | logging:info:2216 - [weight-sync] Using master_addr=10.128.32.46, master_port=59681 +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) 2026-06-07 02:14:08.865 | INFO  | logging:info:2216 - [weight-sync] get_node_ip_address()=10.128.32.46 [repeated 3x across cluster] +(FSDPPolicyWorkerBase pid=2701490) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=2701490) No module named 'vllm._version' [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=2701490) from .version import __version__, __version_tuple__ # isort:skip [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank0]:[W607 02:14:08.715725173 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-46-interconnect-1.jupiter.internal]:59681 (errno: 97 - Address family not supported by protocol). +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:08.938 | INFO  | skyrl_train.fully_async_trainer:_train_loop:544 - Finished: 'init_weight_sync_state', time cost: 5.81s +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:08.938 | ERROR  | skyrl_train.fully_async_trainer:train:493 - Train loop failed at global_step 28: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +(skyrl_entrypoint pid=2701319) Traceback (most recent call last): +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 491, in train +(skyrl_entrypoint pid=2701319) await self._train_loop() +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 545, in _train_loop +(skyrl_entrypoint pid=2701319) self.init_weight_sync_state() +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 851, in init_weight_sync_state +(skyrl_entrypoint pid=2701319) raise RuntimeError( +(skyrl_entrypoint pid=2701319) RuntimeError: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +(RolloutCoordinator pid=2593907, ip=10.128.32.36) [2026-06-07 02:14:09,830 E 2593907 2593949] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:09.964 | INFO  | skyrl_train.inference_engines.inference_engine_client_http_endpoint:shutdown_server:203 - Server shut down after 2 seconds +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:09.964 | INFO  | skyrl_train.trainer:_guarded_sync:226 - HTTP endpoint shutdown complete +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:09.968 | INFO  | skyrl_train.trainer:_guarded_async:215 - Generator shutdown complete +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:09.980 | WARNING  | skyrl_train.trainer:_guarded_async:219 - Inference engine teardown error (non-fatal): The actor died because of an error raised in its creation task, ray::AsyncVLLMInferenceEngine.__init__() (pid=2593284, ip=10.128.32.36, actor_id=a9ee7a598f6c230f2b02b9e202000000, repr=) +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result +(skyrl_entrypoint pid=2701319) return self.__get_result() +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result +(skyrl_entrypoint pid=2701319) raise self._exception +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__ +(skyrl_entrypoint pid=2701319) super().__init__(*args, **kwargs) +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__ +(skyrl_entrypoint pid=2701319) self.llm = self._create_engine(*args, **kwargs) +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine +(skyrl_entrypoint pid=2701319) engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers) +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args +(skyrl_entrypoint pid=2701319) return cls( +(skyrl_entrypoint pid=2701319) ^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__ +(skyrl_entrypoint pid=2701319) self.engine_core = EngineCoreClient.make_async_mp_client( +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client +(skyrl_entrypoint pid=2701319) return AsyncMPClient(*client_args) +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__ +(skyrl_entrypoint pid=2701319) super().__init__( +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__ +(skyrl_entrypoint pid=2701319) with launch_core_engines(vllm_config, executor_class, log_stats) as ( +(skyrl_entrypoint pid=2701319) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__ +(skyrl_entrypoint pid=2701319) next(self.gen) +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines +(skyrl_entrypoint pid=2701319) wait_for_engine_startup( +(skyrl_entrypoint pid=2701319) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup +(skyrl_entrypoint pid=2701319) raise RuntimeError( +(skyrl_entrypoint pid=2701319) RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {} +(skyrl_entrypoint pid=2701319) 2026-06-07 02:14:09.981 | INFO  | skyrl_train.trainer:_kill_ray_actors:248 - Killing policy_model actors... +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:14:09.965 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:363 - Shutting down shared QueueOrchestrator... +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:14:09.965 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:365 - QueueOrchestrator shutdown complete +(RolloutCoordinator pid=2956288, ip=10.128.32.39) 2026-06-07 02:14:09.966 | INFO | examples.terminal_bench.rollout_coordinator:shutdown:258 - [RolloutCoordinator 0] shutdown complete +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py:470: UserWarning: No model update group to destroy +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) warnings.warn("No model update group to destroy") +2026-06-07 02:14:10.027 | ERROR | __main__:main:134 - Training failed: ray::skyrl_entrypoint() (pid=2701319, ip=10.128.32.34) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 105, in skyrl_entrypoint + exp.run() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/entrypoints/main_base.py", line 483, in run + asyncio.run(trainer.train()) + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 195, in run + return runner.run(main) + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 118, in run + return self._loop.run_until_complete(task) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 491, in train + await self._train_loop() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 545, in _train_loop + self.init_weight_sync_state() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 851, in init_weight_sync_state + raise RuntimeError( +RuntimeError: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +2026-06-07 02:14:10.027 | INFO | __main__:main:137 - Shutting down Ray on head node... +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py:470: UserWarning: No model update group to destroy [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) warnings.warn("No model update group to destroy") [repeated 27x across cluster] +(skyrl_entrypoint pid=2701319) [fd-monitor] Started monitoring (every 120s) +(skyrl_entrypoint pid=2701319) [fd-monitor] [02:11:44] OK: 50 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=2701319) [fd-monitor] [02:11:44] OK: RSS 1.38 GiB | node mem 126.9/858.0 GiB used (14.8%), avail 731.1 GiB +(skyrl_entrypoint pid=2701319) ⚙️ Running in WANDB offline mode +(skyrl_entrypoint pid=2701319) INFO 06-07 02:12:04 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:26 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(pid=2666657, ip=10.128.32.38) INFO 06-07 02:12:30 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) WARNING 06-07 02:12:31 [arg_utils.py:1256] The global random seed is set to 42. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:31 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:31 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:31 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:31 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:31 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) WARNING 06-07 02:12:31 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:31 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) WARNING 06-07 02:12:31 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) WARNING 06-07 02:12:31 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(pid=2659764, ip=10.128.32.43) INFO 06-07 02:12:35 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) WARNING 06-07 02:12:34 [arg_utils.py:1256] The global random seed is set to 55. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) INFO 06-07 02:12:34 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) INFO 06-07 02:12:34 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) INFO 06-07 02:12:34 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) INFO 06-07 02:12:34 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) INFO 06-07 02:12:34 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) WARNING 06-07 02:12:34 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) INFO 06-07 02:12:34 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) WARNING 06-07 02:12:34 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666658, ip=10.128.32.38) WARNING 06-07 02:12:34 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) INFO 06-07 02:12:39 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=42, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) INFO 06-07 02:12:39 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) WARNING 06-07 02:12:43 [arg_utils.py:1256] The global random seed is set to 81. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:43 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:43 [model.py:1549] Using max model len 32768 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:43 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:43 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:43 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) WARNING 06-07 02:12:43 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:43 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) WARNING 06-07 02:12:43 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2659897, ip=10.128.32.43) WARNING 06-07 02:12:43 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(pid=2623829, ip=10.128.32.46) ⚙️ Running in WANDB offline mode +(AsyncVLLMInferenceEngine pid=2666657, ip=10.128.32.38) (EngineCore_DP0 pid=2666896) INFO 06-07 02:12:44 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=56, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(pid=2972657, ip=10.128.32.44) INFO 06-07 02:12:44 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 39x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) WARNING 06-07 02:12:48 [arg_utils.py:1256] The global random seed is set to 71. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) INFO 06-07 02:12:48 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) INFO 06-07 02:12:48 [model.py:1549] Using max model len 32768 [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) INFO 06-07 02:12:48 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) INFO 06-07 02:12:48 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) INFO 06-07 02:12:48 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) WARNING 06-07 02:12:48 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=3076171, ip=10.128.32.42) INFO 06-07 02:12:48 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) WARNING 06-07 02:12:48 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) WARNING 06-07 02:12:48 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=2666656, ip=10.128.32.38) (EngineCore_DP0 pid=2666891) INFO 06-07 02:12:50 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) INFO 06-07 02:12:50 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.38:51279 backend=nccl +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) INFO 06-07 02:12:50 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:12:48 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=66, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=2659899, ip=10.128.32.43) (EngineCore_DP0 pid=2660131) INFO 06-07 02:12:51 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=80, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) INFO 06-07 02:12:52 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) INFO 06-07 02:12:51 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 8x across cluster] +(pid=2623902, ip=10.128.32.46) ⚙️ Running in WANDB offline mode +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) WARNING 06-07 02:12:53 [arg_utils.py:1256] The global random seed is set to 86. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:12:53 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:12:53 [model.py:1549] Using max model len 32768 [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:12:53 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:12:53 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:12:53 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) WARNING 06-07 02:12:53 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:12:53 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) WARNING 06-07 02:12:53 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 22x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) WARNING 06-07 02:12:53 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 22x across cluster] +(AsyncVLLMInferenceEngine pid=2666524, ip=10.128.32.38) (EngineCore_DP0 pid=2666870) INFO 06-07 02:12:54 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) INFO 06-07 02:12:55 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) INFO 06-07 02:12:55 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.43:50889 backend=nccl [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) INFO 06-07 02:12:55 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) INFO 06-07 02:12:52 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=81, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) INFO 06-07 02:12:55 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2593411, ip=10.128.32.36) INFO 06-07 02:12:57 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 12x across cluster] +(pid=2701488) ⚙️ Running in WANDB offline mode [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) WARNING 06-07 02:12:54 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) WARNING 06-07 02:12:54 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) (EngineCore_DP0 pid=2660148) INFO 06-07 02:12:56 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2593412, ip=10.128.32.36) (EngineCore_DP0 pid=2593646) INFO 06-07 02:13:00 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2575228, ip=10.128.32.41) (EngineCore_DP0 pid=2575453) INFO 06-07 02:13:01 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.41:58449 backend=nccl +(AsyncVLLMInferenceEngine pid=2575228, ip=10.128.32.41) (EngineCore_DP0 pid=2575453) INFO 06-07 02:13:01 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:00 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=75, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 36x across cluster] +(AsyncVLLMInferenceEngine pid=2575228, ip=10.128.32.41) (EngineCore_DP0 pid=2575453) INFO 06-07 02:13:02 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2955861, ip=10.128.32.39) INFO 06-07 02:13:01 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 25x across cluster] +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] EngineCore failed to start. +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 996, in run_engine_core +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 740, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] super().__init__( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 106, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] self.model_executor = executor_class(vllm_config) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/executor/abstract.py", line 103, in __init__ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] self._init_executor() +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/executor/uniproc_executor.py", line 47, in _init_executor +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] self.driver_worker.init_device() +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/worker/worker_base.py", line 332, in init_device +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] self.worker.init_device() # type: ignore +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/worker/gpu_worker.py", line 232, in init_device +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] init_worker_distributed_environment( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/worker/gpu_worker.py", line 1040, in init_worker_distributed_environment +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] init_distributed_environment( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/distributed/parallel_state.py", line 1256, in init_distributed_environment +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] torch.distributed.init_process_group( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] return func(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 95, in wrapper +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] func_return = func(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py", line 1762, in init_process_group +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] store, rank, world_size = next(rendezvous_iterator) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 230, in _tcp_rendezvous_handler +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] store = _create_c10d_store( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 198, in _create_c10d_store +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] return TCPStore( +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] ^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] torch.distributed.DistNetworkError: The server socket has failed to listen on any local network address. port: 37945, useIpv6: false, code: -98, name: EADDRINUSE, message: address already in use +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] Exception raised from makeWithPort at /pytorch/torch/csrc/distributed/c10d/TCPStoreLibUvBackend.cpp:307 (most recent call first): +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0xb0 (0x4000730cc700 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libc10.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #1: + 0x5f29220 (0x400053269220 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #2: + 0x5f4326c (0x40005328326c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #3: + 0x5f49074 (0x400053289074 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #4: + 0x5f49138 (0x400053289138 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #5: + 0x5f2ccc4 (0x40005326ccc4 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #6: c10d::TCPStore::TCPStore(std::__cxx11::basic_string, std::allocator >, c10d::TCPStoreOptions const&) + 0x104 (0x400053271564 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #7: + 0x109a094 (0x40004d01a094 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #8: + 0x113236c (0x40004d0b236c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #9: + 0x5d6d60 (0x40004c556d60 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #10: + 0x1b7a38 (0xaaaabc307a38 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #11: _PyObject_MakeTpCall + 0x98 (0xaaaabc2b5db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #12: + 0x169f50 (0xaaaabc2b9f50 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #13: + 0x1682e4 (0xaaaabc2b82e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #14: + 0x1e0ce8 (0xaaaabc330ce8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #15: + 0x1d7ddc (0xaaaabc327ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #16: + 0x646b0c (0x40004c5c6b0c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #17: _PyObject_MakeTpCall + 0x98 (0xaaaabc2b5db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #18: _PyEval_EvalFrameDefault + 0x280c (0xaaaabc3bae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #19: + 0x1808c0 (0xaaaabc2d08c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #20: + 0x182bf8 (0xaaaabc2d2bf8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #21: + 0x25fd30 (0xaaaabc3afd30 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #22: + 0x1b7d20 (0xaaaabc307d20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #23: PyObject_Vectorcall + 0x54 (0xaaaabc2b60e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #24: _PyEval_EvalFrameDefault + 0x280c (0xaaaabc3bae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #25: _PyObject_FastCallDictTstate + 0x80 (0xaaaabc2b7f20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #26: _PyObject_Call_Prepend + 0x140 (0xaaaabc2b81ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #27: + 0x1e0d80 (0xaaaabc330d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #28: + 0x1d7ddc (0xaaaabc327ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #29: _PyObject_MakeTpCall + 0x98 (0xaaaabc2b5db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #30: _PyEval_EvalFrameDefault + 0x280c (0xaaaabc3bae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #31: _PyObject_FastCallDictTstate + 0x10c (0xaaaabc2b7fac in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #32: _PyObject_Call_Prepend + 0x140 (0xaaaabc2b81ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #33: + 0x1e0d80 (0xaaaabc330d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #34: + 0x1d7ddc (0xaaaabc327ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #35: _PyObject_Call + 0x68 (0xaaaabc2b8488 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #36: _PyEval_EvalFrameDefault + 0x52ac (0xaaaabc3bd8f4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #37: PyEval_EvalCode + 0xb4 (0xaaaabc3c2eb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #38: + 0x2ccdcc (0xaaaabc41cdcc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #39: + 0x2ccef4 (0xaaaabc41cef4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #40: PyRun_StringFlags + 0x90 (0xaaaabc421050 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #41: PyRun_SimpleStringFlags + 0x58 (0xaaaabc4210f8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #42: Py_RunMain + 0x2c8 (0xaaaabc449190 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #43: Py_BytesMain + 0x64 (0xaaaabc449fb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #44: + 0x27540 (0x40003b5c7540 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #45: __libc_start_main + 0x98 (0x40003b5c7618 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] frame #46: + 0x10e0c0 (0xaaaabc25e0c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2593284, ip=10.128.32.36) (EngineCore_DP0 pid=2593652) ERROR 06-07 02:13:02 [core.py:1006] +(AsyncVLLMInferenceEngine pid=2593411, ip=10.128.32.36) (EngineCore_DP0 pid=2593642) INFO 06-07 02:13:02 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:03 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2972655, ip=10.128.32.44) (EngineCore_DP0 pid=2972907) INFO 06-07 02:13:05 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=984310, ip=10.128.32.37) (EngineCore_DP0 pid=984419) INFO 06-07 02:13:06 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.37:34951 backend=nccl [repeated 35x across cluster] +(AsyncVLLMInferenceEngine pid=984310, ip=10.128.32.37) (EngineCore_DP0 pid=984419) INFO 06-07 02:13:06 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 34x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:03 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=87, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) INFO 06-07 02:13:02 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=984310, ip=10.128.32.37) (EngineCore_DP0 pid=984419) INFO 06-07 02:13:06 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:08 [default_loader.py:293] Loading weights took 13.01 seconds +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 13.474666 seconds +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:11 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:11 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:11 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) (EngineCore_DP0 pid=2956113) INFO 06-07 02:13:08 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) (EngineCore_DP0 pid=2956113) INFO 06-07 02:13:07 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:11 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) (EngineCore_DP0 pid=2956113) INFO 06-07 02:13:07 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.39:38597 backend=nccl [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) (EngineCore_DP0 pid=2956113) INFO 06-07 02:13:07 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:11 [core.py:278] init engine (profile, create kv cache, warmup model) took 3.23 seconds +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) WARNING 06-07 02:13:12 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:12 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) WARNING 06-07 02:13:12 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) (EngineCore_DP0 pid=2660123) INFO 06-07 02:13:12 [vllm.py:846] Cudagraph is disabled under eager mode +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) NCCL version 2.27.7+cuda13.0 +(AsyncVLLMInferenceEngine pid=2659764, ip=10.128.32.43) WARNING 06-07 02:13:12 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=2955732, ip=10.128.32.39) (EngineCore_DP0 pid=2956113) INFO 06-07 02:13:08 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:12 [default_loader.py:293] Loading weights took 7.01 seconds [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:12 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 7.348132 seconds [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:15 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:15 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:15 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) (EngineCore_DP0 pid=2575477) INFO 06-07 02:13:15 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:15 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.79 seconds [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) WARNING 06-07 02:13:16 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:16 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) WARNING 06-07 02:13:16 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575227, ip=10.128.32.41) (EngineCore_DP0 pid=2575458) INFO 06-07 02:13:16 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2575229, ip=10.128.32.41) WARNING 06-07 02:13:16 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2972657, ip=10.128.32.44) (EngineCore_DP0 pid=2972891) INFO 06-07 02:13:20 [default_loader.py:293] Loading weights took 13.29 seconds [repeated 19x across cluster] +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:18 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.779174 seconds [repeated 15x across cluster] +(RolloutCoordinator pid=2956288, ip=10.128.32.39) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=2956288, ip=10.128.32.39) [fd-monitor] [02:13:20] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=2956288, ip=10.128.32.39) [fd-monitor] [02:13:20] OK: RSS 0.78 GiB | node mem 203.7/858.0 GiB used (23.7%), avail 654.3 GiB +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:20 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:20 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:20 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:20 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) INFO 06-07 02:13:20 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.81 seconds [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2793256, ip=10.128.32.45) (EngineCore_DP0 pid=2793492) WARNING 06-07 02:13:21 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) (EngineCore_DP0 pid=2558151) INFO 06-07 02:13:21 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) (EngineCore_DP0 pid=2558151) WARNING 06-07 02:13:21 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2557897, ip=10.128.32.48) (EngineCore_DP0 pid=2558151) INFO 06-07 02:13:21 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2793126, ip=10.128.32.45) WARNING 06-07 02:13:21 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:22 [default_loader.py:293] Loading weights took 13.19 seconds [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:22 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.293649 seconds [repeated 20x across cluster] +(RolloutCoordinator pid=984614, ip=10.128.32.37) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=984614, ip=10.128.32.37) [fd-monitor] [02:13:28] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=984614, ip=10.128.32.37) [fd-monitor] [02:13:28] OK: RSS 0.78 GiB | node mem 389.3/858.0 GiB used (45.4%), avail 468.6 GiB +(AsyncVLLMInferenceEngine pid=2955861, ip=10.128.32.39) (EngineCore_DP0 pid=2956093) INFO 06-07 02:13:25 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955861, ip=10.128.32.39) (EngineCore_DP0 pid=2956093) INFO 06-07 02:13:25 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955861, ip=10.128.32.39) (EngineCore_DP0 pid=2956093) INFO 06-07 02:13:25 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:25 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:25 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.80 seconds [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) WARNING 06-07 02:13:25 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:25 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) WARNING 06-07 02:13:25 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) (EngineCore_DP0 pid=2956097) INFO 06-07 02:13:25 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=2955859, ip=10.128.32.39) WARNING 06-07 02:13:26 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 20x across cluster] +(RolloutCoordinator pid=2660382, ip=10.128.32.43) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=2660382, ip=10.128.32.43) [fd-monitor] [02:13:36] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=2660382, ip=10.128.32.43) [fd-monitor] [02:13:36] OK: RSS 0.78 GiB | node mem 390.8/858.0 GiB used (45.5%), avail 467.2 GiB +(skyrl_entrypoint pid=2701319) [fd-monitor] [02:13:44] OK: 101 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=2701319) [fd-monitor] [02:13:44] OK: RSS 1.64 GiB | node mem 213.7/858.0 GiB used (24.9%), avail 644.2 GiB +(RolloutCoordinator pid=2593907, ip=10.128.32.36) [fd-monitor] Started monitoring (every 120s) +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Loading model from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy/model_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Loading extra_state from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy/extra_state_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Loading optim from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy/optim_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Successfully loaded model state dict +(RolloutCoordinator pid=2593907, ip=10.128.32.36) [fd-monitor] [02:13:45] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=2593907, ip=10.128.32.36) [fd-monitor] [02:13:45] OK: RSS 0.78 GiB | node mem 324.8/858.0 GiB used (37.9%), avail 533.2 GiB +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Successfully loaded optimizer state +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Successfully loaded scheduler state +(FSDPPolicyWorkerBase pid=2623829, ip=10.128.32.46) [rank-0]: Checkpoint loaded successfully from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy +(FSDPPolicyWorkerBase pid=2701489) INFO 06-07 02:14:06 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2659898, ip=10.128.32.43) ERROR 06-07 02:14:10 [core_client.py:616] Engine core proc EngineCore_DP0 died unexpectedly, shutting down client. +(FSDPPolicyWorkerBase pid=2623904, ip=10.128.32.46) INFO 06-07 02:14:07 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 7x across cluster] +Error executing job with overrides: ['+terminal_bench_config=terminal_bench', 'trainer.strategy=fsdp2', 'trainer.algorithm.advantage_estimator=rloo_n', 'trainer.algorithm.use_kl_loss=false', 'trainer.algorithm.kl_loss_coef=0.0', 'trainer.algorithm.eps_clip_low=0.2', 'trainer.algorithm.eps_clip_high=0.05', 'trainer.algorithm.loss_reduction=sequence_mean', 'trainer.epochs=2', 'trainer.max_steps=80', 'trainer.update_epochs_per_batch=1', 'trainer.train_batch_size=64', 'trainer.policy_mini_batch_size=64', 'trainer.eval_batch_size=64', 'trainer.micro_forward_batch_size_per_gpu=4', 'trainer.micro_train_batch_size_per_gpu=1', 'trainer.max_prompt_length=999999', 'trainer.eval_interval=999999', 'trainer.eval_before_train=false', 'trainer.ckpt_interval=2', 'trainer.resume_mode=latest', 'trainer.hf_save_interval=5', '++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0', '++trainer.hf_hub_private=false', '++trainer.hf_hub_revision=main', '++trainer.enable_db_registration=false', 'trainer.project_name=OpenThoughts-Agent', 'trainer.log_level=INFO', 'trainer.tracker_commit_each_step=true', 'trainer.logger=console', 'trainer.run_name=ablation-pymethods2test-seqmean-arm0', 'trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints', 'trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports', 'trainer.policy.optimizer_config.lr=8e-6', 'trainer.policy.optimizer_config.weight_decay=0.0', 'trainer.policy.optimizer_config.adam_betas=[0.9,0.999]', 'trainer.policy.optimizer_config.max_grad_norm=0.9', 'trainer.policy.fsdp_config.cpu_offload=false', 'trainer.policy.fsdp_config.reshard_after_forward=true', 'trainer.policy.fsdp_config.fsdp_size=4', 'trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'trainer.ref.fsdp_config.cpu_offload=false', 'trainer.ref.fsdp_config.reshard_after_forward=true', 'trainer.ref.fsdp_config.fsdp_size=4', 'trainer.placement.colocate_all=false', 'trainer.placement.policy_num_nodes=2', 'trainer.placement.ref_num_nodes=2', 'trainer.placement.policy_num_gpus_per_node=4', 'trainer.placement.ref_num_gpus_per_node=4', 'trainer.fully_async.max_staleness_steps=16', 'trainer.fully_async.num_parallel_generation_workers=338', 'generator.backend=vllm', 'generator.timeout_multiplier=1.0', 'generator.model_dtype=bfloat16', 'generator.inference_engine_tensor_parallel_size=1', 'generator.num_inference_engines=48', 'generator.n_samples_per_prompt=8', 'generator.eval_n_samples_per_prompt=8', 'generator.gpu_memory_utilization=0.75', 'generator.max_num_seqs=24', 'generator.max_num_batched_tokens=65536', 'generator.enable_prefix_caching=true', 'generator.enable_chunked_prefill=true', 'generator.run_engines_locally=true', 'generator.weight_sync_backend=nccl', 'generator.async_engine=true', 'generator.batched=false', 'generator.enable_http_endpoint=true', 'generator.enable_ray_prometheus_stats=false', 'generator.vllm_stats_interval=1', 'generator.append_eos_token_after_stop_str_in_multi_turn=true', 'generator.max_turns=999999', 'generator.sampling_params.max_generate_length=4096', 'generator.sampling_params.temperature=0.7', 'generator.sampling_params.top_p=0.95', 'generator.sampling_params.top_k=20', '++generator.engine_init_kwargs.max_model_len=32768', '++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2', '++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"]', 'data.val_data=[]', '+terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs', '+terminal_bench_config.harbor.name=terminus-2', '+terminal_bench_config.harbor.max_episodes=999999', '+terminal_bench_config.harbor.enable_summarize=false', '+terminal_bench_config.harbor.store_all_messages=true', '+terminal_bench_config.harbor.trajectory_config.raw_content=true', '+terminal_bench_config.harbor.enable_episode_logging=false', '+terminal_bench_config.harbor.record_terminal_session=false', '+terminal_bench_config.harbor.enable_pane_logging=false', '+terminal_bench_config.harbor.strict_json_parser=true', '+terminal_bench_config.harbor.interleaved_thinking=true', '+terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true', '+terminal_bench_config.harbor.override_timeout_sec=900', '+terminal_bench_config.harbor.override_cpus=1', '+terminal_bench_config.harbor.override_memory_mb=2048', '+terminal_bench_config.harbor.override_storage_mb=2048', '+terminal_bench_config.harbor.auto_snapshot=true', '+terminal_bench_config.harbor.verifier_override_timeout_sec=120', '+terminal_bench_config.harbor.max_retries=3', '+terminal_bench_config.harbor.min_wait_sec=60.0', '+terminal_bench_config.harbor.max_wait_sec=600.0', '+terminal_bench_config.harbor.wait_multiplier=2.0', '+terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"]', '+terminal_bench_config.harbor.n_concurrent_trials=675', '+terminal_bench_config.harbor.log_level=INFO', '+terminal_bench_config.harbor.enable_reward_shaping=false', '+terminal_bench_config.harbor.enable_error_classification=true', '+terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"]', '+terminal_bench_config.harbor.default_error_treatment=zero', '+terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"]', '+terminal_bench_config.harbor.zero_exceptions=[]', '+terminal_bench_config.model_info.max_input_tokens=32000', '+terminal_bench_config.model_info.max_output_tokens=4096', '+terminal_bench_config.archiving.enabled=false', '+terminal_bench_config.trace_upload.enabled=true', '+terminal_bench_config.trace_upload.repo_org=DCAgent', '+terminal_bench_config.trace_upload.episodes=last', '+terminal_bench_config.trace_upload.dataset_type=SFT', '+terminal_bench_config.trace_upload.cleanup=true'] +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 142, in + main() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/main.py", line 94, in decorated_main + _run_hydra( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra + _run_app( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 457, in _run_app + run_and_report( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 223, in run_and_report + raise ex + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 220, in run_and_report + return func() + ^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 458, in + lambda: hydra.run( + ^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/hydra.py", line 132, in run + _ = ret.return_value + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 260, in return_value + raise self._return_value + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 186, in run_job + ret.return_value = task_function(task_cfg) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 132, in main + ray.get(skyrl_entrypoint.remote(cfg)) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 2967, in get + values, debugger_breakpoint = worker.get_objects( + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 1015, in get_objects + raise value.as_instanceof_cause() +ray.exceptions.RayTaskError(RuntimeError): ray::skyrl_entrypoint() (pid=2701319, ip=10.128.32.34) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 105, in skyrl_entrypoint + exp.run() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/entrypoints/main_base.py", line 483, in run + asyncio.run(trainer.train()) + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 195, in run + return runner.run(main) + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 118, in run + return self._loop.run_until_complete(task) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 491, in train + await self._train_loop() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 545, in _train_loop + self.init_weight_sync_state() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 851, in init_weight_sync_state + raise RuntimeError( +RuntimeError: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +Stopping Ray cluster... +Ray cluster stopped +[RLJobRunner] Crash detected (exit!=0) — preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/ray_logs BEFORE trace upload (so a wall-clock kill can't lose crash evidence)... + Collecting Ray logs from worker jpbo-041-36... + Collecting Ray logs from worker jpbo-041-37... + Collecting Ray logs from worker jpbo-041-38... + Collecting Ray logs from worker jpbo-041-39... + Collecting Ray logs from worker jpbo-041-40... + Collecting Ray logs from worker jpbo-041-41... + Collecting Ray logs from worker jpbo-041-42... + Collecting Ray logs from worker jpbo-041-43... + Collecting Ray logs from worker jpbo-041-44... + Collecting Ray logs from worker jpbo-041-45... + Collecting Ray logs from worker jpbo-041-46... + Collecting Ray logs from worker jpbo-041-47... + Collecting Ray logs from worker jpbo-041-48... +[RLJobRunner] Crash-time Ray log preservation complete. +[RLJobRunner] Launching trace upload (training exit code: 1): + repo_id: DCAgent/ablation-pymethods2test-seqmean-arm0 + job_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0 + episodes: last + log: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/logs/ablation-pymethods2test-seqmean-arm0_trace_upload.log +[RLJobRunner] Waiting for trace upload to complete... +[RLJobRunner] Trace upload failed with exit code 1. +Preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ray_logs/ + Collecting Ray logs from worker jpbo-041-36... + Collecting Ray logs from worker jpbo-041-37... + Collecting Ray logs from worker jpbo-041-38... + Collecting Ray logs from worker jpbo-041-39... + Collecting Ray logs from worker jpbo-041-40... + Collecting Ray logs from worker jpbo-041-41... + Collecting Ray logs from worker jpbo-041-42... + Collecting Ray logs from worker jpbo-041-43... + Collecting Ray logs from worker jpbo-041-44... + Collecting Ray logs from worker jpbo-041-45... + Collecting Ray logs from worker jpbo-041-46... + Collecting Ray logs from worker jpbo-041-47... + Collecting Ray logs from worker jpbo-041-48... +Ray log preservation complete diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630126.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630126.out new file mode 100644 index 0000000..7bfd8b2 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630126.out @@ -0,0 +1,1665 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Activating RL environment: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl +Python executable: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python path check: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +[ray] RAY_TMPDIR=/tmp/ray/ray_630126 +[triton_cache] Triton cache: /tmp/triton_cache_feuer1_630126 +[triton_cache] TorchInductor cache: /tmp/torchinductor_cache_feuer1_630126 +[proxy] ✓ Found proxychains binary at /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 +[proxy] Setting up SSH tunnel to jpbl-s01-01 +[proxy] SSH key: /e/home/jusers/feuer1/jupiter/.ssh/authorized_keys/id_ed25519_jsc +[proxy] Tunnel port: 7003 +[proxy] Node IP: 10.128.32.33 (workers will connect here) +[proxy] ✓ SSH tunnel started successfully +[proxy] ✓ Generated proxychains config at /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630126.conf +[proxy] - Internal traffic (10.x.x.x, 172.x.x.x, 169.254.x.x) → DIRECT +[proxy] - External traffic (internet) → PROXY via tunnel +[proxy] ✓ Daytona timeout settings configured +[proxy] Testing proxy connectivity... +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630126.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[proxy] ✓ Proxy connectivity test passed (huggingface.co reachable via wrapped binary) +[proxy] ⚠ Tunnel not accessible at 10.128.32.33:7003 (workers may fail) +[proxy] ✓ Proxy setup complete (using wrapped binary for Ray workers) +[container_runtime] Using cloud backend: daytona (no local container setup) +=== Universal RL Training Runner === +Config: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/configs/ablation-pymethods2test-seqmean-arm0_rl_config.json +Working directory: /e/scratch/jureap59/feuer1/OpenThoughts-Agent +Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python version: Python 3.12.12 +UV_USE_IO_URING: 0 +Proxy: DISABLED (direct internet or not configured) +======================================== +=== RLJobRunner: ablation-pymethods2test-seqmean-arm0 === +[wandb_utils] Fixing permissions on: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +[wandb_utils] WandB directory ready: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb + HF_TOKEN=****pDbg + HF_HUB_CACHE=/e/data1/datasets/playground/ot-baf/hf_hub + SUPABASE_URL=https://rpzmyuapoqilpghynmza.s... (direct Supabase config) +Environment configured: + TENSOR_PARALLEL_SIZE=1 + NUM_INFERENCE_ENGINES=56 + POLICY_NUM_NODES=14 + WANDB_DIR=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +Starting Ray cluster with 14 nodes, 4 GPUs/node +Cleaning up existing Ray instances... +=== Starting Ray Cluster === + Nodes: 14 + GPUs per node: 4 + CPUs per node: 288 + Head node: jpbo-041-33 (10.128.32.33) + Ray port: 6379 +============================ + Starting Ray head on jpbo-041-33 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_head_jpbo-041-33.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.33 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-33 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --head --node-ip-address=10.128.32.33 --port=6379 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray head on jpbo-041-33 + Starting Ray worker on jpbo-041-34 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-34.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.34 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-34 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.34 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 1 on jpbo-041-34 + Starting Ray worker on jpbo-041-35 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-35.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.35 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-35 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.35 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 2 on jpbo-041-35 + Starting Ray worker on jpbo-041-36 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-36.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.36 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-36 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.36 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 3 on jpbo-041-36 + Starting Ray worker on jpbo-041-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.37 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.37 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 4 on jpbo-041-37 + Starting Ray worker on jpbo-041-38 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-38.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.38 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-38 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.38 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 5 on jpbo-041-38 + Starting Ray worker on jpbo-041-39 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-39.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.39 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-39 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.39 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 6 on jpbo-041-39 + Starting Ray worker on jpbo-041-40 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-40.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.40 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-40 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.40 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 7 on jpbo-041-40 + Starting Ray worker on jpbo-041-41 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-41.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.41 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-41 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.41 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 8 on jpbo-041-41 + Starting Ray worker on jpbo-041-42 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-42.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.42 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-42 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.42 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 9 on jpbo-041-42 + Starting Ray worker on jpbo-041-43 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-43.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.43 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-43 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.43 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 10 on jpbo-041-43 + Starting Ray worker on jpbo-041-44 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-44.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.44 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-44 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.44 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 11 on jpbo-041-44 + Starting Ray worker on jpbo-041-45 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-45.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.45 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-45 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.45 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 12 on jpbo-041-45 + Starting Ray worker on jpbo-041-46 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-46.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.46 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-46 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.46 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 13 on jpbo-041-46 + Waiting for cluster (56 GPUs, 14 nodes)... +Connecting to Ray at 10.128.32.33:6379 (expecting 14 nodes, 56.0 GPUs) +Ray connection established, polling for resources... +[Ray wait] nodes=14/14 GPUs=56.0/56.0 resources={'GPU': 56.0, 'memory': 10652272558080.0, 'accelerator_type:GH200': 14.0, 'object_store_memory': 601295421440.0, 'CPU': 4032.0, 'node:10.128.32.35': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.39': 1.0, 'node:10.128.32.41': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.37': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.43': 1.0} +✓ Ray cluster ready +=== Ray Cluster Ready === + Address: 10.128.32.33:6379 + Total GPUs: 56 +========================= +Ray cluster ready at 10.128.32.33:6379 +Total GPUs available: 56 +[RLJobRunner] Pinggy check: url=False, token=False, needs_tunnel=False (agent=terminus-2, env=daytona) +[RLJobRunner] No Pinggy tunnel needed, using local vLLM + +Running SkyRL: + Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python + Entrypoint: examples.terminal_bench.entrypoints.main_tbench + Args: 120 Hydra arguments + Working dir: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train +Using proxychains binary: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 + +Executing command with srun: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f $PROXYCHAINS_CONF_FILE /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python -m examples.terminal_bench.entrypoints.main_tbench +terminal_bench_config=terminal_bench trainer.strategy=fsdp2 trainer.algorithm.advantage_estimator=rloo_n trainer.algorithm.use_kl_loss=false trainer.algorithm.kl_loss_coef=0.0 trainer.algorithm.eps_clip_low=0.2 trainer.algorithm.eps_clip_high=0.05 trainer.algorithm.loss_reduction=sequence_mean trainer.epochs=2 trainer.max_steps=80 trainer.update_epochs_per_batch=1 trainer.train_batch_size=64 trainer.policy_mini_batch_size=64 trainer.eval_batch_size=64 trainer.micro_forward_batch_size_per_gpu=4 trainer.micro_train_batch_size_per_gpu=1 trainer.max_prompt_length=999999 trainer.eval_interval=999999 trainer.eval_before_train=false trainer.ckpt_interval=2 trainer.resume_mode=latest trainer.hf_save_interval=5 ++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0 ++trainer.hf_hub_private=false ++trainer.hf_hub_revision=main ++trainer.enable_db_registration=false trainer.project_name=OpenThoughts-Agent trainer.log_level=INFO trainer.tracker_commit_each_step=true trainer.logger=console trainer.run_name=ablation-pymethods2test-seqmean-arm0 trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports trainer.policy.optimizer_config.lr=8e-6 trainer.policy.optimizer_config.weight_decay=0.0 trainer.policy.optimizer_config.adam_betas=[0.9,0.999] trainer.policy.optimizer_config.max_grad_norm=0.9 trainer.policy.fsdp_config.cpu_offload=false trainer.policy.fsdp_config.reshard_after_forward=true trainer.policy.fsdp_config.fsdp_size=4 trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 trainer.ref.fsdp_config.cpu_offload=false trainer.ref.fsdp_config.reshard_after_forward=true trainer.ref.fsdp_config.fsdp_size=4 trainer.placement.colocate_all=false trainer.placement.policy_num_nodes=2 trainer.placement.ref_num_nodes=2 trainer.placement.policy_num_gpus_per_node=4 trainer.placement.ref_num_gpus_per_node=4 trainer.fully_async.max_staleness_steps=16 trainer.fully_async.num_parallel_generation_workers=338 generator.backend=vllm generator.timeout_multiplier=1.0 generator.model_dtype=bfloat16 generator.inference_engine_tensor_parallel_size=1 generator.num_inference_engines=48 generator.n_samples_per_prompt=8 generator.eval_n_samples_per_prompt=8 generator.gpu_memory_utilization=0.75 generator.max_num_seqs=24 generator.max_num_batched_tokens=65536 generator.enable_prefix_caching=true generator.enable_chunked_prefill=true generator.run_engines_locally=true generator.weight_sync_backend=nccl generator.async_engine=true generator.batched=false generator.enable_http_endpoint=true generator.enable_ray_prometheus_stats=false generator.vllm_stats_interval=1 generator.append_eos_token_after_stop_str_in_multi_turn=true generator.max_turns=999999 generator.sampling_params.max_generate_length=4096 generator.sampling_params.temperature=0.7 generator.sampling_params.top_p=0.95 generator.sampling_params.top_k=20 ++generator.engine_init_kwargs.max_model_len=32768 ++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2 ++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"] data.val_data=[] +terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs +terminal_bench_config.harbor.name=terminus-2 +terminal_bench_config.harbor.max_episodes=999999 +terminal_bench_config.harbor.enable_summarize=false +terminal_bench_config.harbor.store_all_messages=true +terminal_bench_config.harbor.trajectory_config.raw_content=true +terminal_bench_config.harbor.enable_episode_logging=false +terminal_bench_config.harbor.record_terminal_session=false +terminal_bench_config.harbor.enable_pane_logging=false +terminal_bench_config.harbor.strict_json_parser=true +terminal_bench_config.harbor.interleaved_thinking=true +terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true +terminal_bench_config.harbor.override_timeout_sec=900 +terminal_bench_config.harbor.override_cpus=1 +terminal_bench_config.harbor.override_memory_mb=2048 +terminal_bench_config.harbor.override_storage_mb=2048 +terminal_bench_config.harbor.auto_snapshot=true +terminal_bench_config.harbor.verifier_override_timeout_sec=120 +terminal_bench_config.harbor.max_retries=3 +terminal_bench_config.harbor.min_wait_sec=60.0 +terminal_bench_config.harbor.max_wait_sec=600.0 +terminal_bench_config.harbor.wait_multiplier=2.0 +terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"] +terminal_bench_config.harbor.n_concurrent_trials=675 +terminal_bench_config.harbor.log_level=INFO +terminal_bench_config.harbor.enable_reward_shaping=false +terminal_bench_config.harbor.enable_error_classification=true +terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"] +terminal_bench_config.harbor.default_error_treatment=zero +terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"] +terminal_bench_config.harbor.zero_exceptions=[] +terminal_bench_config.model_info.max_input_tokens=32000 +terminal_bench_config.model_info.max_output_tokens=4096 +terminal_bench_config.archiving.enabled=false +terminal_bench_config.trace_upload.enabled=true +terminal_bench_config.trace_upload.repo_org=DCAgent +terminal_bench_config.trace_upload.episodes=last +terminal_bench_config.trace_upload.dataset_type=SFT +terminal_bench_config.trace_upload.cleanup=true +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630126.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-07 02:20:53.422 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:686 - Exporting wandb api key to ray runtime env +2026-06-07 02:20:53.423 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:705 - Exporting RAY_ADDRESS to ray runtime env +2026-06-07 02:20:53.423 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:730 - Exporting `NCCL_SOCKET_IFNAME` to ray runtime env: ib0 +2026-06-07 02:20:53.423 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:730 - Exporting `NCCL_SOCKET_FAMILY` to ray runtime env: AF_INET +2026-06-07 02:20:53.423 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:730 - Exporting `NCCL_DEBUG` to ray runtime env: WARN +2026-06-07 02:20:53,423 INFO worker.py:1680 -- Using address 10.128.32.33:6379 set in the environment variable RAY_ADDRESS +2026-06-07 02:20:53,456 INFO worker.py:1821 -- Connecting to existing Ray cluster at address: 10.128.32.33:6379... +2026-06-07 02:20:53,467 INFO worker.py:2007 -- Connected to Ray cluster. +/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0 + warnings.warn( +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-07 02:20:55.689 | INFO | skyrl_train.utils.ppo_utils:sync_registries:546 - Synced registries to ray actor +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:02.936 | INFO  | skyrl_train.entrypoints.main_base:_configure_log_level:212 - SkyRL log level set to: INFO +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 17x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) +(skyrl_entrypoint pid=294281) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:03.364 | INFO  | examples.terminal_bench.dataset:_load_data_files:40 - Loading data from: /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:06.531 | INFO  | examples.terminal_bench.dataset:_load_data_files:50 - Found 5000 valid task directories out of 5000 total directories +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:06.532 | INFO  | examples.terminal_bench.dataset:__init__:27 - TerminalBenchTaskDataset initialized with 5000 task paths +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:06.545 | INFO  | skyrl_train.entrypoints.main_base:_setup_trainer:405 - data: +(skyrl_entrypoint pid=294281) train_data: +(skyrl_entrypoint pid=294281) - /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=294281) val_data: [] +(skyrl_entrypoint pid=294281) trainer: +(skyrl_entrypoint pid=294281) placement: +(skyrl_entrypoint pid=294281) colocate_all: false +(skyrl_entrypoint pid=294281) colocate_policy_ref: true +(skyrl_entrypoint pid=294281) policy_num_nodes: 2 +(skyrl_entrypoint pid=294281) policy_num_gpus_per_node: 4 +(skyrl_entrypoint pid=294281) critic_num_nodes: 1 +(skyrl_entrypoint pid=294281) critic_num_gpus_per_node: 4 +(skyrl_entrypoint pid=294281) ref_num_nodes: 2 +(skyrl_entrypoint pid=294281) ref_num_gpus_per_node: 4 +(skyrl_entrypoint pid=294281) policy_strict_spread_pg: false +(skyrl_entrypoint pid=294281) policy_per_gpu_bundles: false +(skyrl_entrypoint pid=294281) sequence_parallel_backend: ulysses +(skyrl_entrypoint pid=294281) strategy: fsdp2 +(skyrl_entrypoint pid=294281) policy: +(skyrl_entrypoint pid=294281) model: +(skyrl_entrypoint pid=294281) path: /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=294281) lora: +(skyrl_entrypoint pid=294281) rank: 0 +(skyrl_entrypoint pid=294281) alpha: 16 +(skyrl_entrypoint pid=294281) dropout: 0 +(skyrl_entrypoint pid=294281) lora_sync_path: /tmp/skyrl_lora_sync +(skyrl_entrypoint pid=294281) target_modules: all-linear +(skyrl_entrypoint pid=294281) exclude_modules: null +(skyrl_entrypoint pid=294281) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=294281) optimizer_config: +(skyrl_entrypoint pid=294281) optimizer: AdamW +(skyrl_entrypoint pid=294281) lr: 8.0e-06 +(skyrl_entrypoint pid=294281) adam_betas: +(skyrl_entrypoint pid=294281) - 0.9 +(skyrl_entrypoint pid=294281) - 0.999 +(skyrl_entrypoint pid=294281) weight_decay: 0.0 +(skyrl_entrypoint pid=294281) max_grad_norm: 0.9 +(skyrl_entrypoint pid=294281) offload_after_step: true +(skyrl_entrypoint pid=294281) num_warmup_steps: 0 +(skyrl_entrypoint pid=294281) scheduler: constant_with_warmup +(skyrl_entrypoint pid=294281) optimizer_kwargs: {} +(skyrl_entrypoint pid=294281) fsdp_config: +(skyrl_entrypoint pid=294281) cpu_offload: false +(skyrl_entrypoint pid=294281) reshard_after_forward: true +(skyrl_entrypoint pid=294281) fsdp_size: 4 +(skyrl_entrypoint pid=294281) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=294281) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=294281) moe_router_replay: false +(skyrl_entrypoint pid=294281) moe_grouped_gemm: false +(skyrl_entrypoint pid=294281) ep_comm_backend: torch +(skyrl_entrypoint pid=294281) deepep_num_sms: 20 +(skyrl_entrypoint pid=294281) deepep_token_chunk_size: null +(skyrl_entrypoint pid=294281) sequence_parallel_size: 1 +(skyrl_entrypoint pid=294281) use_torch_compile: false +(skyrl_entrypoint pid=294281) record_memory: false +(skyrl_entrypoint pid=294281) megatron_config: +(skyrl_entrypoint pid=294281) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) context_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_tensor_parallel_size: null +(skyrl_entrypoint pid=294281) ddp_config: +(skyrl_entrypoint pid=294281) grad_reduce_in_fp32: true +(skyrl_entrypoint pid=294281) overlap_grad_reduce: false +(skyrl_entrypoint pid=294281) overlap_param_gather: false +(skyrl_entrypoint pid=294281) average_in_collective: true +(skyrl_entrypoint pid=294281) model_config_kwargs: {} +(skyrl_entrypoint pid=294281) torch_profiler_config: +(skyrl_entrypoint pid=294281) enable: false +(skyrl_entrypoint pid=294281) ranks: [] +(skyrl_entrypoint pid=294281) save_path: null +(skyrl_entrypoint pid=294281) optimizer_config_kwargs: +(skyrl_entrypoint pid=294281) overlap_cpu_optimizer_d2h_h2d: false +(skyrl_entrypoint pid=294281) use_precision_aware_optimizer: false +(skyrl_entrypoint pid=294281) optimizer_cpu_offload: false +(skyrl_entrypoint pid=294281) optimizer_offload_fraction: 0.0 +(skyrl_entrypoint pid=294281) transformer_config_kwargs: +(skyrl_entrypoint pid=294281) recompute_granularity: full +(skyrl_entrypoint pid=294281) recompute_modules: +(skyrl_entrypoint pid=294281) - core_attn +(skyrl_entrypoint pid=294281) recompute_method: uniform +(skyrl_entrypoint pid=294281) recompute_num_layers: 1 +(skyrl_entrypoint pid=294281) empty_cuda_cache: true +(skyrl_entrypoint pid=294281) ref: +(skyrl_entrypoint pid=294281) model: +(skyrl_entrypoint pid=294281) path: ${trainer.policy.model.path} +(skyrl_entrypoint pid=294281) sequence_parallel_size: 1 +(skyrl_entrypoint pid=294281) deepspeed_config: ${deepspeed_config.eval} +(skyrl_entrypoint pid=294281) fsdp_config: +(skyrl_entrypoint pid=294281) cpu_offload: false +(skyrl_entrypoint pid=294281) reshard_after_forward: true +(skyrl_entrypoint pid=294281) fsdp_size: 4 +(skyrl_entrypoint pid=294281) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=294281) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=294281) moe_router_replay: false +(skyrl_entrypoint pid=294281) moe_grouped_gemm: false +(skyrl_entrypoint pid=294281) ep_comm_backend: torch +(skyrl_entrypoint pid=294281) deepep_num_sms: 20 +(skyrl_entrypoint pid=294281) deepep_token_chunk_size: null +(skyrl_entrypoint pid=294281) megatron_config: +(skyrl_entrypoint pid=294281) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) context_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=294281) model_config_kwargs: {} +(skyrl_entrypoint pid=294281) transformer_config_kwargs: {} +(skyrl_entrypoint pid=294281) critic: +(skyrl_entrypoint pid=294281) model: +(skyrl_entrypoint pid=294281) path: null +(skyrl_entrypoint pid=294281) lora: +(skyrl_entrypoint pid=294281) rank: 0 +(skyrl_entrypoint pid=294281) alpha: 16 +(skyrl_entrypoint pid=294281) dropout: 0 +(skyrl_entrypoint pid=294281) target_modules: all-linear +(skyrl_entrypoint pid=294281) exclude_modules: null +(skyrl_entrypoint pid=294281) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=294281) optimizer_config: +(skyrl_entrypoint pid=294281) optimizer: AdamW +(skyrl_entrypoint pid=294281) lr: 5.0e-06 +(skyrl_entrypoint pid=294281) adam_betas: +(skyrl_entrypoint pid=294281) - 0.9 +(skyrl_entrypoint pid=294281) - 0.999 +(skyrl_entrypoint pid=294281) weight_decay: 0.01 +(skyrl_entrypoint pid=294281) max_grad_norm: 1.0 +(skyrl_entrypoint pid=294281) offload_after_step: true +(skyrl_entrypoint pid=294281) num_warmup_steps: 0 +(skyrl_entrypoint pid=294281) scheduler: constant_with_warmup +(skyrl_entrypoint pid=294281) optimizer_kwargs: {} +(skyrl_entrypoint pid=294281) fsdp_config: +(skyrl_entrypoint pid=294281) cpu_offload: false +(skyrl_entrypoint pid=294281) reshard_after_forward: true +(skyrl_entrypoint pid=294281) fsdp_size: -1 +(skyrl_entrypoint pid=294281) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=294281) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=294281) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=294281) moe_router_replay: false +(skyrl_entrypoint pid=294281) moe_grouped_gemm: false +(skyrl_entrypoint pid=294281) ep_comm_backend: torch +(skyrl_entrypoint pid=294281) deepep_num_sms: 20 +(skyrl_entrypoint pid=294281) deepep_token_chunk_size: null +(skyrl_entrypoint pid=294281) sequence_parallel_size: 1 +(skyrl_entrypoint pid=294281) algorithm: +(skyrl_entrypoint pid=294281) advantage_estimator: rloo_n +(skyrl_entrypoint pid=294281) kl_ctrl: +(skyrl_entrypoint pid=294281) type: fixed +(skyrl_entrypoint pid=294281) kl_target: 0.1 +(skyrl_entrypoint pid=294281) horizon: 10000 +(skyrl_entrypoint pid=294281) kl_estimator_type: k3 +(skyrl_entrypoint pid=294281) use_kl_estimator_k3: false +(skyrl_entrypoint pid=294281) use_abs_kl: false +(skyrl_entrypoint pid=294281) use_kl_in_reward: false +(skyrl_entrypoint pid=294281) use_kl_loss: false +(skyrl_entrypoint pid=294281) kl_loss_coef: 0.0 +(skyrl_entrypoint pid=294281) use_entropy_loss: false +(skyrl_entrypoint pid=294281) entropy_loss_coef: 0.01 +(skyrl_entrypoint pid=294281) advantage_batch_normalize: false +(skyrl_entrypoint pid=294281) value_head_prefix: value_head +(skyrl_entrypoint pid=294281) policy_loss_type: regular +(skyrl_entrypoint pid=294281) loss_reduction: sequence_mean +(skyrl_entrypoint pid=294281) global_loss_denom: null +(skyrl_entrypoint pid=294281) grpo_norm_by_std: true +(skyrl_entrypoint pid=294281) rloo_n_min_group_size: 4 +(skyrl_entrypoint pid=294281) rloo_n_filter_zero_reward_groups: true +(skyrl_entrypoint pid=294281) lambd: 1.0 +(skyrl_entrypoint pid=294281) gamma: 1.0 +(skyrl_entrypoint pid=294281) eps_clip_low: 0.2 +(skyrl_entrypoint pid=294281) eps_clip_high: 0.05 +(skyrl_entrypoint pid=294281) clip_ratio_c: 3.0 +(skyrl_entrypoint pid=294281) tis_imp_ratio_cap: -1.0 +(skyrl_entrypoint pid=294281) use_tis: false +(skyrl_entrypoint pid=294281) sapo: +(skyrl_entrypoint pid=294281) tau_pos: 1.0 +(skyrl_entrypoint pid=294281) tau_neg: 1.05 +(skyrl_entrypoint pid=294281) value_clip: 0.2 +(skyrl_entrypoint pid=294281) dynamic_sampling: +(skyrl_entrypoint pid=294281) type: null +(skyrl_entrypoint pid=294281) max_sample_batches: 30 +(skyrl_entrypoint pid=294281) min_replace_ratio: 0.3 +(skyrl_entrypoint pid=294281) clip_cov: +(skyrl_entrypoint pid=294281) clip_ratio: 0.0002 +(skyrl_entrypoint pid=294281) clip_cov_lb: 1.0 +(skyrl_entrypoint pid=294281) clip_cov_ub: 5.0 +(skyrl_entrypoint pid=294281) kl_cov: +(skyrl_entrypoint pid=294281) kl_cov_frac: 0.2 +(skyrl_entrypoint pid=294281) ppo_kl_coef: 1.0 +(skyrl_entrypoint pid=294281) cispo: +(skyrl_entrypoint pid=294281) cispo_eps_clip_low: 0 +(skyrl_entrypoint pid=294281) cispo_eps_clip_high: 5 +(skyrl_entrypoint pid=294281) z_clip: +(skyrl_entrypoint pid=294281) enabled: false +(skyrl_entrypoint pid=294281) alpha: 0.97 +(skyrl_entrypoint pid=294281) z_thresh: 2.5 +(skyrl_entrypoint pid=294281) warmup_steps: 25 +(skyrl_entrypoint pid=294281) mode: zscore +(skyrl_entrypoint pid=294281) clip_option: adaptive_scaling +(skyrl_entrypoint pid=294281) clip_factor: 1.0 +(skyrl_entrypoint pid=294281) skip_update_on_spike: false +(skyrl_entrypoint pid=294281) stale_clip: +(skyrl_entrypoint pid=294281) enabled: false +(skyrl_entrypoint pid=294281) alpha: 0.3 +(skyrl_entrypoint pid=294281) entropy_threshold: 0.15 +(skyrl_entrypoint pid=294281) entropy_window: 10 +(skyrl_entrypoint pid=294281) min_lr_scale: 0.1 +(skyrl_entrypoint pid=294281) max_seq_len: 1004095 +(skyrl_entrypoint pid=294281) fully_async: +(skyrl_entrypoint pid=294281) max_staleness_steps: 16 +(skyrl_entrypoint pid=294281) num_parallel_generation_workers: 338 +(skyrl_entrypoint pid=294281) gradient_checkpointing: true +(skyrl_entrypoint pid=294281) gradient_checkpointing_use_reentrant: false +(skyrl_entrypoint pid=294281) seed: 42 +(skyrl_entrypoint pid=294281) resume_mode: latest +(skyrl_entrypoint pid=294281) resume_path: null +(skyrl_entrypoint pid=294281) ckpt_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints +(skyrl_entrypoint pid=294281) max_ckpts_to_keep: -1 +(skyrl_entrypoint pid=294281) ckpt_interval: 2 +(skyrl_entrypoint pid=294281) hf_save_interval: 5 +(skyrl_entrypoint pid=294281) hf_upload_mode: latest +(skyrl_entrypoint pid=294281) export_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports +(skyrl_entrypoint pid=294281) bf16: true +(skyrl_entrypoint pid=294281) epochs: 2 +(skyrl_entrypoint pid=294281) max_steps: 80 +(skyrl_entrypoint pid=294281) update_epochs_per_batch: 1 +(skyrl_entrypoint pid=294281) train_batch_size: 64 +(skyrl_entrypoint pid=294281) policy_mini_batch_size: 64 +(skyrl_entrypoint pid=294281) critic_mini_batch_size: 256 +(skyrl_entrypoint pid=294281) micro_train_batch_size_per_gpu: 1 +(skyrl_entrypoint pid=294281) micro_forward_batch_size_per_gpu: 4 +(skyrl_entrypoint pid=294281) update_ref_every_epoch: false +(skyrl_entrypoint pid=294281) use_sample_packing: true +(skyrl_entrypoint pid=294281) eval_batch_size: 64 +(skyrl_entrypoint pid=294281) eval_before_train: false +(skyrl_entrypoint pid=294281) eval_interval: 999999 +(skyrl_entrypoint pid=294281) max_prompt_length: 999999 +(skyrl_entrypoint pid=294281) flash_attn: true +(skyrl_entrypoint pid=294281) disable_fast_tokenizer: false +(skyrl_entrypoint pid=294281) target_modules: null +(skyrl_entrypoint pid=294281) exclude_modules: null +(skyrl_entrypoint pid=294281) project_name: OpenThoughts-Agent +(skyrl_entrypoint pid=294281) run_name: ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=294281) logger: console +(skyrl_entrypoint pid=294281) tracker_commit_each_step: true +(skyrl_entrypoint pid=294281) dump_data_batch: false +(skyrl_entrypoint pid=294281) dump_eval_results: true +(skyrl_entrypoint pid=294281) log_level: INFO +(skyrl_entrypoint pid=294281) rope_scaling: null +(skyrl_entrypoint pid=294281) rope_theta: null +(skyrl_entrypoint pid=294281) step_wise_training: false +(skyrl_entrypoint pid=294281) hf_hub_repo_id: laion/ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=294281) hf_hub_private: false +(skyrl_entrypoint pid=294281) hf_hub_revision: main +(skyrl_entrypoint pid=294281) enable_db_registration: false +(skyrl_entrypoint pid=294281) generator: +(skyrl_entrypoint pid=294281) model_name: ${trainer.policy.model.path} +(skyrl_entrypoint pid=294281) model_dtype: bfloat16 +(skyrl_entrypoint pid=294281) timeout_multiplier: 1.0 +(skyrl_entrypoint pid=294281) run_engines_locally: true +(skyrl_entrypoint pid=294281) num_inference_engines: 48 +(skyrl_entrypoint pid=294281) backend: vllm +(skyrl_entrypoint pid=294281) weight_sync_backend: nccl +(skyrl_entrypoint pid=294281) fuse_weights: false +(skyrl_entrypoint pid=294281) weight_transfer_threshold_cuda_ipc_GB: 1.0 +(skyrl_entrypoint pid=294281) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=294281) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=294281) inference_engine_expert_parallel_size: 1 +(skyrl_entrypoint pid=294281) inference_engine_data_parallel_size: 1 +(skyrl_entrypoint pid=294281) n_samples_per_prompt: 8 +(skyrl_entrypoint pid=294281) async_engine: true +(skyrl_entrypoint pid=294281) batched: false +(skyrl_entrypoint pid=294281) max_input_length: ${trainer.max_prompt_length} +(skyrl_entrypoint pid=294281) vllm_v1_disable_multiproc: true +(skyrl_entrypoint pid=294281) enable_prefix_caching: true +(skyrl_entrypoint pid=294281) enable_chunked_prefill: true +(skyrl_entrypoint pid=294281) max_num_batched_tokens: 65536 +(skyrl_entrypoint pid=294281) enforce_eager: true +(skyrl_entrypoint pid=294281) fully_sharded_loras: false +(skyrl_entrypoint pid=294281) enable_ray_prometheus_stats: false +(skyrl_entrypoint pid=294281) vllm_stats_interval: 1 +(skyrl_entrypoint pid=294281) gpu_memory_utilization: 0.75 +(skyrl_entrypoint pid=294281) max_num_seqs: 24 +(skyrl_entrypoint pid=294281) remote_inference_engine_urls: +(skyrl_entrypoint pid=294281) - 127.0.0.1:8001 +(skyrl_entrypoint pid=294281) enable_http_endpoint: true +(skyrl_entrypoint pid=294281) http_endpoint_host: 127.0.0.1 +(skyrl_entrypoint pid=294281) http_endpoint_port: 8000 +(skyrl_entrypoint pid=294281) max_turns: 999999 +(skyrl_entrypoint pid=294281) chat_template: +(skyrl_entrypoint pid=294281) source: name +(skyrl_entrypoint pid=294281) name_or_path: null +(skyrl_entrypoint pid=294281) chat_template_kwargs: {} +(skyrl_entrypoint pid=294281) engine_init_kwargs: +(skyrl_entrypoint pid=294281) max_model_len: 32768 +(skyrl_entrypoint pid=294281) custom_chat_template_chat_completion_path: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=294281) served_model_name: 0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=294281) override_existing_update_group: disable +(skyrl_entrypoint pid=294281) sampling_params: +(skyrl_entrypoint pid=294281) max_generate_length: 4096 +(skyrl_entrypoint pid=294281) repetition_penalty: 1.0 +(skyrl_entrypoint pid=294281) temperature: 0.7 +(skyrl_entrypoint pid=294281) top_p: 0.95 +(skyrl_entrypoint pid=294281) min_p: 0.0 +(skyrl_entrypoint pid=294281) top_k: 20 +(skyrl_entrypoint pid=294281) logprobs: null +(skyrl_entrypoint pid=294281) stop: null +(skyrl_entrypoint pid=294281) use_conversation_multi_turn: true +(skyrl_entrypoint pid=294281) append_eos_token_after_stop_str_in_multi_turn: true +(skyrl_entrypoint pid=294281) eval_sampling_params: +(skyrl_entrypoint pid=294281) max_generate_length: ${generator.sampling_params.max_generate_length} +(skyrl_entrypoint pid=294281) repetition_penalty: 1.0 +(skyrl_entrypoint pid=294281) temperature: 0.0 +(skyrl_entrypoint pid=294281) top_p: 1.0 +(skyrl_entrypoint pid=294281) min_p: 0.0 +(skyrl_entrypoint pid=294281) top_k: -1 +(skyrl_entrypoint pid=294281) logprobs: null +(skyrl_entrypoint pid=294281) stop: null +(skyrl_entrypoint pid=294281) eval_n_samples_per_prompt: 8 +(skyrl_entrypoint pid=294281) zero_reward_on_non_stop: false +(skyrl_entrypoint pid=294281) apply_overlong_filtering: false +(skyrl_entrypoint pid=294281) rope_scaling: ${trainer.rope_scaling} +(skyrl_entrypoint pid=294281) rope_theta: ${trainer.rope_theta} +(skyrl_entrypoint pid=294281) teacher: +(skyrl_entrypoint pid=294281) model_path: null +(skyrl_entrypoint pid=294281) top_k_logprobs: 256 +(skyrl_entrypoint pid=294281) num_inference_engines: 1 +(skyrl_entrypoint pid=294281) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=294281) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=294281) gpu_memory_utilization: 0.9 +(skyrl_entrypoint pid=294281) enforce_eager: false +(skyrl_entrypoint pid=294281) backend: vllm +(skyrl_entrypoint pid=294281) engine_init_kwargs: {} +(skyrl_entrypoint pid=294281) environment: +(skyrl_entrypoint pid=294281) env_class: gsm8k +(skyrl_entrypoint pid=294281) skyrl_gym: +(skyrl_entrypoint pid=294281) max_env_workers: 32 +(skyrl_entrypoint pid=294281) text2sql: +(skyrl_entrypoint pid=294281) db_path: /home/ray/default/sql_data +(skyrl_entrypoint pid=294281) llm_as_a_judge: +(skyrl_entrypoint pid=294281) model: gpt-4o-mini +(skyrl_entrypoint pid=294281) base_url: null +(skyrl_entrypoint pid=294281) search: +(skyrl_entrypoint pid=294281) log_requests: false +(skyrl_entrypoint pid=294281) search_url: http://127.0.0.1:8000/retrieve +(skyrl_entrypoint pid=294281) topk: 3 +(skyrl_entrypoint pid=294281) timeout: 30 +(skyrl_entrypoint pid=294281) rollout: +(skyrl_entrypoint pid=294281) fanout: +(skyrl_entrypoint pid=294281) enabled: true +(skyrl_entrypoint pid=294281) num_coordinators: 4 +(skyrl_entrypoint pid=294281) cpus_per_coordinator: 8 +(skyrl_entrypoint pid=294281) deepspeed_config: +(skyrl_entrypoint pid=294281) train: +(skyrl_entrypoint pid=294281) zero_optimization: +(skyrl_entrypoint pid=294281) stage: 3 +(skyrl_entrypoint pid=294281) offload_param: +(skyrl_entrypoint pid=294281) device: none +(skyrl_entrypoint pid=294281) offload_optimizer: +(skyrl_entrypoint pid=294281) device: none +(skyrl_entrypoint pid=294281) pin_memory: true +(skyrl_entrypoint pid=294281) sub_group_size: auto +(skyrl_entrypoint pid=294281) reduce_bucket_size: auto +(skyrl_entrypoint pid=294281) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=294281) stage3_prefetch_bucket_size: auto +(skyrl_entrypoint pid=294281) stage3_max_live_parameters: auto +(skyrl_entrypoint pid=294281) stage3_max_reuse_distance: auto +(skyrl_entrypoint pid=294281) round_robin_gradients: true +(skyrl_entrypoint pid=294281) zero_hpz_partition_size: 1 +(skyrl_entrypoint pid=294281) zero_quantized_weights: false +(skyrl_entrypoint pid=294281) zero_quantized_gradients: false +(skyrl_entrypoint pid=294281) torch_autocast: +(skyrl_entrypoint pid=294281) enabled: true +(skyrl_entrypoint pid=294281) dtype: bfloat16 +(skyrl_entrypoint pid=294281) disable_trace_cache: false +(skyrl_entrypoint pid=294281) data_types: +(skyrl_entrypoint pid=294281) grad_accum_dtype: fp32 +(skyrl_entrypoint pid=294281) gradient_clipping: 1.0 +(skyrl_entrypoint pid=294281) wall_clock_breakdown: false +(skyrl_entrypoint pid=294281) prescale_gradient: false +(skyrl_entrypoint pid=294281) eval: +(skyrl_entrypoint pid=294281) zero_optimization: +(skyrl_entrypoint pid=294281) stage: 3 +(skyrl_entrypoint pid=294281) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=294281) offload_param: +(skyrl_entrypoint pid=294281) device: cpu +(skyrl_entrypoint pid=294281) pin_memory: true +(skyrl_entrypoint pid=294281) torch_autocast: +(skyrl_entrypoint pid=294281) enabled: true +(skyrl_entrypoint pid=294281) dtype: bfloat16 +(skyrl_entrypoint pid=294281) gradient_clipping: 1.0 +(skyrl_entrypoint pid=294281) prescale_gradient: false +(skyrl_entrypoint pid=294281) wall_clock_breakdown: false +(skyrl_entrypoint pid=294281) terminal_bench_config: +(skyrl_entrypoint pid=294281) trials_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs +(skyrl_entrypoint pid=294281) harbor: +(skyrl_entrypoint pid=294281) name: terminus-2 +(skyrl_entrypoint pid=294281) max_episodes: 999999 +(skyrl_entrypoint pid=294281) enable_summarize: false +(skyrl_entrypoint pid=294281) store_all_messages: true +(skyrl_entrypoint pid=294281) trajectory_config: +(skyrl_entrypoint pid=294281) raw_content: true +(skyrl_entrypoint pid=294281) enable_episode_logging: false +(skyrl_entrypoint pid=294281) record_terminal_session: false +(skyrl_entrypoint pid=294281) enable_pane_logging: false +(skyrl_entrypoint pid=294281) strict_json_parser: true +(skyrl_entrypoint pid=294281) interleaved_thinking: true +(skyrl_entrypoint pid=294281) extra_body: +(skyrl_entrypoint pid=294281) chat_template_kwargs: +(skyrl_entrypoint pid=294281) enable_thinking: true +(skyrl_entrypoint pid=294281) override_timeout_sec: 900 +(skyrl_entrypoint pid=294281) override_cpus: 1 +(skyrl_entrypoint pid=294281) override_memory_mb: 2048 +(skyrl_entrypoint pid=294281) override_storage_mb: 2048 +(skyrl_entrypoint pid=294281) auto_snapshot: true +(skyrl_entrypoint pid=294281) verifier_override_timeout_sec: 120 +(skyrl_entrypoint pid=294281) max_retries: 3 +(skyrl_entrypoint pid=294281) min_wait_sec: 60.0 +(skyrl_entrypoint pid=294281) max_wait_sec: 600.0 +(skyrl_entrypoint pid=294281) wait_multiplier: 2.0 +(skyrl_entrypoint pid=294281) exclude_exceptions: +(skyrl_entrypoint pid=294281) - VerifierTimeoutError +(skyrl_entrypoint pid=294281) - VerifierRuntimeError +(skyrl_entrypoint pid=294281) - RewardFileNotFoundError +(skyrl_entrypoint pid=294281) - RewardFileEmptyError +(skyrl_entrypoint pid=294281) - VerifierOutputParseError +(skyrl_entrypoint pid=294281) n_concurrent_trials: 675 +(skyrl_entrypoint pid=294281) log_level: INFO +(skyrl_entrypoint pid=294281) enable_reward_shaping: false +(skyrl_entrypoint pid=294281) enable_error_classification: true +(skyrl_entrypoint pid=294281) mask_exceptions: +(skyrl_entrypoint pid=294281) - DaytonaError +(skyrl_entrypoint pid=294281) - EnvironmentStartTimeoutError +(skyrl_entrypoint pid=294281) - NetworkError +(skyrl_entrypoint pid=294281) - ConnectionError +(skyrl_entrypoint pid=294281) - RewardFileNotFoundError +(skyrl_entrypoint pid=294281) - RewardFileEmptyError +(skyrl_entrypoint pid=294281) - AgentEnvironmentTimeoutError +(skyrl_entrypoint pid=294281) - ContextLengthExceededError +(skyrl_entrypoint pid=294281) default_error_treatment: zero +(skyrl_entrypoint pid=294281) passthrough_exceptions: +(skyrl_entrypoint pid=294281) - AgentTimeoutError +(skyrl_entrypoint pid=294281) zero_exceptions: [] +(skyrl_entrypoint pid=294281) model_info: +(skyrl_entrypoint pid=294281) max_input_tokens: 32000 +(skyrl_entrypoint pid=294281) max_output_tokens: 4096 +(skyrl_entrypoint pid=294281) archiving: +(skyrl_entrypoint pid=294281) enabled: false +(skyrl_entrypoint pid=294281) trace_upload: +(skyrl_entrypoint pid=294281) enabled: true +(skyrl_entrypoint pid=294281) repo_org: DCAgent +(skyrl_entrypoint pid=294281) episodes: last +(skyrl_entrypoint pid=294281) dataset_type: SFT +(skyrl_entrypoint pid=294281) cleanup: true +(skyrl_entrypoint pid=294281)  +(skyrl_entrypoint pid=294281) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(skyrl_entrypoint pid=294281) No module named 'vllm._version' +(skyrl_entrypoint pid=294281) from .version import __version__, __version_tuple__ # isort:skip +(skyrl_entrypoint pid=294281) W0607 02:21:22.569000 294281 envs/rl/lib/python3.12/site-packages/torch/utils/cpp_extension.py:117] No CUDA runtime is found, using CUDA_HOME='/e/software/default/stages/2026/software/CUDA/13' +[2026-06-07 02:21:23,669 E 293833 294255] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RegistryActor pid=987007, ip=10.128.32.37) [2026-06-07 02:21:24,551 E 987007 987047] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(skyrl_entrypoint pid=294281) [2026-06-07 02:21:27,013 E 294281 294324] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 2x across cluster] +(pid=987079, ip=10.128.32.37) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=987079, ip=10.128.32.37) No module named 'vllm._version' +(pid=987079, ip=10.128.32.37) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.32.40) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 41x across cluster] +(pid=2596339, ip=10.128.32.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 7x across cluster] +(pid=2596339, ip=10.128.32.36) No module named 'vllm._version' [repeated 7x across cluster] +(pid=2596339, ip=10.128.32.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 7x across cluster] +(raylet, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 111x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:40.638 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:40.638 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:40.639 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:41.259 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:41.284 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:41.284 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:41.285 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:41.285 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) 2026-06-07 02:21:41.305 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.18s (attempt 1/5) to avoid port collisions +(pid=2704166, ip=10.128.32.34) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 13x across cluster] +(pid=2704166, ip=10.128.32.34) No module named 'vllm._version' [repeated 13x across cluster] +(pid=2704166, ip=10.128.32.34) from .version import __version__, __version_tuple__ # isort:skip [repeated 13x across cluster] +(raylet, ip=10.128.32.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 69x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:45.976 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=1, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:45.976 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:45.976 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=987214, ip=10.128.32.37) 2026-06-07 02:21:46.027 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=987214, ip=10.128.32.37) 2026-06-07 02:21:46.028 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=987214, ip=10.128.32.37) 2026-06-07 02:21:46.028 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:46.972 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 72-143 (NUMA node 1) for GPU 1 +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:46.976 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 1 +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:46.976 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:46.976 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:46.976 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) 2026-06-07 02:21:47.003 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 1.81s (attempt 1/5) to avoid port collisions +(pid=2958820, ip=10.128.32.39) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 10x across cluster] +(pid=2958820, ip=10.128.32.39) No module named 'vllm._version' [repeated 10x across cluster] +(pid=2958820, ip=10.128.32.39) from .version import __version__, __version_tuple__ # isort:skip [repeated 10x across cluster] +(raylet, ip=10.128.32.45) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 63x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(skyrl_entrypoint pid=294281) [2026-06-07 02:21:50] INFO inference_engine_client_http_endpoint.py:350: Starting server on 0.0.0.0:8000 +(skyrl_entrypoint pid=294281) [2026-06-07 02:21:50] INFO inference_engine_client_http_endpoint.py:242: Starting inference HTTP endpoint... +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:50.121 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:50.122 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:50.122 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 17x across cluster] +(skyrl_entrypoint pid=294281) [2026-06-07 02:21:51] INFO inference_engine_client_http_endpoint.py:229: Server ready after 2 attempts (2 seconds) +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.391 | INFO  | skyrl_train.inference_engines.inference_engine_client:_spin_up_http_endpoint:969 - InferenceEngineClient HTTP endpoint started on 127.0.0.1:8000 +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.391 | INFO  | skyrl_train.inference_engines.inference_engine_client:__init__:61 - InferenceEngineClient initialized with 48 engines. +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.394 | INFO  | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.395 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 675. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.408 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.409 | INFO  | skyrl_train.utils.trainer_utils:build_dataloader:656 - Total steps: 156 +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.409 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:357 - Length of train_dataloader: 5000 +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.409 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:358 - Number of steps per epoch: 78 +(skyrl_entrypoint pid=294281) 2026-06-07 02:21:51.409 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:359 - Total training steps: 80 +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:51.407 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:51.427 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:51.427 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:51.427 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:51.427 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) 2026-06-07 02:21:51.452 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.18s (attempt 1/5) to avoid port collisions [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) No module named 'vllm._version' [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) from .version import __version__, __version_tuple__ # isort:skip [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 120x across cluster] +(AsyncVLLMInferenceEngine pid=3079062, ip=10.128.32.42) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 17x across cluster] +(pid=2662729, ip=10.128.32.43) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. +(AsyncVLLMInferenceEngine pid=2626656, ip=10.128.32.46) 2026-06-07 02:21:55.378 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:123 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=1, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626656, ip=10.128.32.46) 2026-06-07 02:21:55.380 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626656, ip=10.128.32.46) 2026-06-07 02:21:55.380 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) [W607 02:21:56.447607181 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-37.jupiter.internal]:41139 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) [W607 02:21:56.449335629 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) [rank0]:[W607 02:21:56.451880032 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:21:56.650 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:21:56.652 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:21:56.652 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:21:56.652 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:21:56.652 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:21:56.667 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.60s (attempt 1/5) to avoid port collisions [repeated 9x across cluster] +(bundle_reservation_check_func pid=294359) [2026-06-07 02:21:57,860 E 294359 294399] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(AsyncVLLMInferenceEngine pid=2704166, ip=10.128.32.34) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2704166, ip=10.128.32.34) No module named 'vllm._version' [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2704166, ip=10.128.32.34) from .version import __version__, __version_tuple__ # isort:skip [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2704294, ip=10.128.32.34) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:21:58.939 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:21:58.939 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) [W607 02:22:01.433309818 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-37.jupiter.internal]:50831 (errno: 97 - Address family not supported by protocol). [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) [W607 02:22:01.433831729 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) [rank0]:[W607 02:22:01.436403587 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:02<00:08, 2.86s/it] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:22:00.236 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:22:00.261 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:22:00.261 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:22:00.261 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:22:00.261 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) 2026-06-07 02:22:00.294 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 1.55s (attempt 1/5) to avoid port collisions [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2542123, ip=10.128.32.40) (EngineCore_DP0 pid=2542234) _C._set_float32_matmul_precision(precision) +(pid=2662807, ip=10.128.32.43) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) [2026-06-07 02:22:01,958 E 2541854 2541963] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2541994, ip=10.128.32.40) (EngineCore_DP0 pid=2542240) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) No module named 'vllm._version' [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) from .version import __version__, __version_tuple__ # isort:skip [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 72x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 26x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:01.691 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:01.691 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2596338, ip=10.128.32.36) (EngineCore_DP0 pid=2596579) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3079063, ip=10.128.32.42) (EngineCore_DP0 pid=3079292) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3079063, ip=10.128.32.42) (EngineCore_DP0 pid=3079292) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2596340, ip=10.128.32.36) (EngineCore_DP0 pid=2596570) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2596340, ip=10.128.32.36) [W607 02:22:06.608280396 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-36.jupiter.internal]:36675 (errno: 97 - Address family not supported by protocol). [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2596340, ip=10.128.32.36) [W607 02:22:06.608742019 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2596340, ip=10.128.32.36) [rank0]:[W607 02:22:06.611067956 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) Loading safetensors checkpoint shards: 75% Completed | 3/4 [00:07<00:02, 2.64s/it] [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) (EngineCore_DP0 pid=2669786) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) (EngineCore_DP0 pid=2669802) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:02.951 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 72-143 (NUMA node 1) for GPU 1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:02.975 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:02.975 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:02.975 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:02.976 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:03.008 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 2.80s (attempt 1/5) to avoid port collisions [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3078934, ip=10.128.32.42) (EngineCore_DP0 pid=3079317) _C._set_float32_matmul_precision(precision) +(pid=294462) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) [2026-06-07 02:22:07,570 E 2669552 2669716] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=3079062, ip=10.128.32.42) (EngineCore_DP0 pid=3079296) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) +(AsyncVLLMInferenceEngine pid=987214, ip=10.128.32.37) (EngineCore_DP0 pid=987460) +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) +(AsyncVLLMInferenceEngine pid=2669550, ip=10.128.32.38) (EngineCore_DP0 pid=2669785) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) No module named 'vllm._version' [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) from .version import __version__, __version_tuple__ # isort:skip [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) (EngineCore_DP0 pid=2669802) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:13.432 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:13.432 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) (EngineCore_DP0 pid=2975787) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) (EngineCore_DP0 pid=2626889) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2958819, ip=10.128.32.39) (EngineCore_DP0 pid=2959046) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:14.790 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:14.816 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:14.816 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:14.817 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:14.817 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:14.850 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 1.65s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2975429, ip=10.128.32.44) (EngineCore_DP0 pid=2975795) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2704295, ip=10.128.32.34) (EngineCore_DP0 pid=2704527) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00, std::allocator >) + 0xb0 (0x40006f4ac700 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libc10.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #1: + 0x5f29220 (0x40004f649220 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #2: + 0x5f4326c (0x40004f66326c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #3: + 0x5f49074 (0x40004f669074 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #4: + 0x5f49138 (0x40004f669138 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #5: + 0x5f2ccc4 (0x40004f64ccc4 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #6: c10d::TCPStore::TCPStore(std::__cxx11::basic_string, std::allocator >, c10d::TCPStoreOptions const&) + 0x104 (0x40004f651564 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #7: + 0x109a094 (0x4000493fa094 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #8: + 0x113236c (0x40004949236c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #9: + 0x5d6d60 (0x400048936d60 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #10: + 0x1b7a38 (0xaaaab8697a38 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #11: _PyObject_MakeTpCall + 0x98 (0xaaaab8645db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #12: + 0x169f50 (0xaaaab8649f50 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #13: + 0x1682e4 (0xaaaab86482e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #14: + 0x1e0ce8 (0xaaaab86c0ce8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #15: + 0x1d7ddc (0xaaaab86b7ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #16: + 0x646b0c (0x4000489a6b0c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #17: _PyObject_MakeTpCall + 0x98 (0xaaaab8645db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #18: _PyEval_EvalFrameDefault + 0x280c (0xaaaab874ae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #19: + 0x1808c0 (0xaaaab86608c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #20: + 0x182bf8 (0xaaaab8662bf8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #21: + 0x25fd30 (0xaaaab873fd30 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #22: + 0x1b7d20 (0xaaaab8697d20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #23: PyObject_Vectorcall + 0x54 (0xaaaab86460e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #24: _PyEval_EvalFrameDefault + 0x280c (0xaaaab874ae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #25: _PyObject_FastCallDictTstate + 0x80 (0xaaaab8647f20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #26: _PyObject_Call_Prepend + 0x140 (0xaaaab86481ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #27: + 0x1e0d80 (0xaaaab86c0d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #28: + 0x1d7ddc (0xaaaab86b7ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #29: _PyObject_MakeTpCall + 0x98 (0xaaaab8645db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #30: _PyEval_EvalFrameDefault + 0x280c (0xaaaab874ae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #31: _PyObject_FastCallDictTstate + 0x10c (0xaaaab8647fac in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #32: _PyObject_Call_Prepend + 0x140 (0xaaaab86481ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #33: + 0x1e0d80 (0xaaaab86c0d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #34: + 0x1d7ddc (0xaaaab86b7ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #35: _PyObject_Call + 0x68 (0xaaaab8648488 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #36: _PyEval_EvalFrameDefault + 0x52ac (0xaaaab874d8f4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #37: PyEval_EvalCode + 0xb4 (0xaaaab8752eb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #38: + 0x2ccdcc (0xaaaab87acdcc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #39: + 0x2ccef4 (0xaaaab87acef4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #40: PyRun_StringFlags + 0x90 (0xaaaab87b1050 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #41: PyRun_SimpleStringFlags + 0x58 (0xaaaab87b10f8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #42: Py_RunMain + 0x2c8 (0xaaaab87d9190 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #43: Py_BytesMain + 0x64 (0xaaaab87d9fb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #44: + 0x27540 (0x4000379a7540 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #45: __libc_start_main + 0x98 (0x4000379a7618 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) frame #46: + 0x10e0c0 (0xaaaab85ee0c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) [pynccl] dumped 0 entries to /e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace_pynccl_pid2959070 (reason=atexit) +(AsyncVLLMInferenceEngine pid=2975564, ip=10.128.32.44) (EngineCore_DP0 pid=2975800) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) [W607 02:22:16.342198091 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-44.jupiter.internal]:41207 (errno: 97 - Address family not supported by protocol). [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) [W607 02:22:16.342706915 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) [rank0]:[W607 02:22:16.344851394 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) Exception raised in creation task: The actor died because of an error raised in its creation task, ray::AsyncVLLMInferenceEngine.__init__() (pid=2958691, ip=10.128.32.39, actor_id=9f591a871969d2b79a9e310202000000, repr=) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) return self.__get_result() +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) raise self._exception +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) super().__init__(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) self.llm = self._create_engine(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) return cls( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) self.engine_core = EngineCoreClient.make_async_mp_client( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) return AsyncMPClient(*client_args) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) super().__init__( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) with launch_core_engines(vllm_config, executor_class, log_stats) as ( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) next(self.gen) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) wait_for_engine_startup( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) raise RuntimeError( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {} +(AsyncVLLMInferenceEngine pid=2796151, ip=10.128.32.45) (EngineCore_DP0 pid=2796398) Loading safetensors checkpoint shards: 75% Completed | 3/4 [00:03<00:01, 1.26s/it] [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) [pynccl] dumped 0 entries to /e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace_pynccl_pid2958691 (reason=atexit) +(FSDPPolicyWorkerBase pid=294462) `torch_dtype` is deprecated! Use `dtype` instead! [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2796151, ip=10.128.32.45) (EngineCore_DP0 pid=2796398) +(FSDPPolicyWorkerBase pid=294463) Loading checkpoint shards: 0%| | 0/4 [00:00 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:13.432 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:133 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:13.432 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:136 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) (EngineCore_DP0 pid=2578420) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) +(AsyncVLLMInferenceEngine pid=2796016, ip=10.128.32.45) (EngineCore_DP0 pid=2796354) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2541994, ip=10.128.32.40) (EngineCore_DP0 pid=2542240) +(AsyncVLLMInferenceEngine pid=2541995, ip=10.128.32.40) (EngineCore_DP0 pid=2542222) +(AsyncVLLMInferenceEngine pid=2542123, ip=10.128.32.40) (EngineCore_DP0 pid=2542234) +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:14.788 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:14.816 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:14.816 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:584 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:14.816 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:592 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:14.816 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1158 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) 2026-06-07 02:22:14.850 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1210 - Engine startup stagger: sleeping 1.60s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578050, ip=10.128.32.41) (EngineCore_DP0 pid=2578406) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 10.128.32.33 (routable head IP) for coordinator litellm base_url connectivity +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:27.021 | INFO  | examples.terminal_bench.rollout_coordinator:__init__:405 - [RolloutDispatcher] configured num_coordinators=4, cpus_per_coordinator=8 +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:27.059 | INFO  | examples.terminal_bench.rollout_coordinator:startup:434 - [RolloutDispatcher] PlacementGroup ready: 4 bundles x 8 CPU (SPREAD) +(raylet, ip=10.128.32.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(FSDPPolicyWorkerBase pid=294462) [rank7]:[W607 02:22:24.789186688 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) Loading safetensors checkpoint shards: 75% Completed | 3/4 [00:11<00:03, 3.91s/it] [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=2958820, ip=10.128.32.39) (EngineCore_DP0 pid=2959054) +(AsyncVLLMInferenceEngine pid=2958818, ip=10.128.32.39) (EngineCore_DP0 pid=2959050) +(AsyncVLLMInferenceEngine pid=2958819, ip=10.128.32.39) (EngineCore_DP0 pid=2959046) +(AsyncVLLMInferenceEngine pid=2704166, ip=10.128.32.34) (EngineCore_DP0 pid=2704543) +(AsyncVLLMInferenceEngine pid=2704295, ip=10.128.32.34) (EngineCore_DP0 pid=2704527) +(AsyncVLLMInferenceEngine pid=2704294, ip=10.128.32.34) (EngineCore_DP0 pid=2704539) +(AsyncVLLMInferenceEngine pid=2704296, ip=10.128.32.34) (EngineCore_DP0 pid=2704525) +(AsyncVLLMInferenceEngine pid=2626529, ip=10.128.32.46) (EngineCore_DP0 pid=2626901) +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) 2026-06-07 02:22:27.290 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) (EngineCore_DP0 pid=2626889) +(AsyncVLLMInferenceEngine pid=2626656, ip=10.128.32.46) (EngineCore_DP0 pid=2626885) +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) +(AsyncVLLMInferenceEngine pid=2975429, ip=10.128.32.44) (EngineCore_DP0 pid=2975795) +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2975564, ip=10.128.32.44) (EngineCore_DP0 pid=2975800) +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) (EngineCore_DP0 pid=2975787) +(FSDPPolicyWorkerBase pid=2662807, ip=10.128.32.43) [2026-06-07 02:22:30,830 E 2662807 2662947] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.049 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.261 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.261 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.261 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.261 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 0/4] constructed (http=10.128.32.33:8000) +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.265 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.265 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=2669976, ip=10.128.32.38) 2026-06-07 02:22:32.265 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 0] startup complete +(raylet, ip=10.128.32.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 5x across cluster] +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:32.266 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 1/4 started +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) (EngineCore_DP0 pid=2975787) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.28s/it] [repeated 38x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) 2026-06-07 02:22:32.541 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2578050, ip=10.128.32.41) (EngineCore_DP0 pid=2578406) +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) (EngineCore_DP0 pid=2578420) +(AsyncVLLMInferenceEngine pid=2578178, ip=10.128.32.41) (EngineCore_DP0 pid=2578410) +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) [W607 02:22:35.858670330 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-35.jupiter.internal]:33029 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) [W607 02:22:35.862483909 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) [rank0]:[W607 02:22:35.868173189 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 168 (// 4) +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) 2026-06-07 02:22:38.742 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 3x across cluster] +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:39.941 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 2/4 started +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.937 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.937 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.937 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.937 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 1/4] constructed (http=10.128.32.33:8000) +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.940 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.940 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:22:39.940 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 1] startup complete +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) [W607 02:22:35.208951015 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-35-interconnect-1.jupiter.internal]:40633 (errno: 97 - Address family not supported by protocol). [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) [W607 02:22:35.209434236 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) [rank0]:[W607 02:22:35.211722249 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 168 (// 4) +(RolloutCoordinator pid=2596821, ip=10.128.32.36) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.257 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.257 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.258 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.258 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 2/4] constructed (http=10.128.32.33:8000) +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.260 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.261 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=2596821, ip=10.128.32.36) 2026-06-07 02:22:48.261 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 2] startup complete +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:48.262 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 3/4 started +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.36s/it] [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) 2026-06-07 02:22:54.394 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.126 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.36s/it] [repeated 6x across cluster] +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 4x across cluster] +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.354 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 4/4 started +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.354 | INFO  | examples.terminal_bench.rollout_coordinator:startup:477 - [RolloutDispatcher] 4 coordinators started +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.354 | INFO  | skyrl_train.fully_async_trainer:train:485 - Generator startup complete +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.354 | INFO  | skyrl_train.fully_async_trainer:_train_loop:510 - Started: 'load_checkpoints' +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.363 | INFO  | skyrl_train.trainer:load_checkpoints:1664 - Loading checkpoint from: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28 +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.363 | INFO  | skyrl_train.trainer:load_checkpoints:1670 - Resuming from global_step: 28 +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.383 | INFO  | skyrl_train.trainer:load_checkpoints:1686 - Successfully loaded trainer state +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.349 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:236 - Harbor logging level set to INFO +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.350 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:136 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.350 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:152 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.350 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 3/4] constructed (http=10.128.32.33:8000) +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.353 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:292 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.353 | INFO | examples.terminal_bench.terminal_bench_generator:startup:249 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:22:56.353 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 3] startup complete +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.392 | INFO  | skyrl_train.trainer:load_checkpoints:1696 - Successfully loaded dataloader state +(skyrl_entrypoint pid=294281) 2026-06-07 02:22:56.392 | INFO  | skyrl_train.trainer:load_checkpoints:1705 - Loading policy checkpoint from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy +(RolloutCoordinator pid=2669976, ip=10.128.32.38) [2026-06-07 02:22:57,810 E 2669976 2670068] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RolloutCoordinator pid=987693, ip=10.128.32.37) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) 2026-06-07 02:22:54.477 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1267 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 3x across cluster] +(RolloutCoordinator pid=2542476, ip=10.128.32.40) [2026-06-07 02:23:05,084 E 2542476 2542516] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RolloutCoordinator pid=2596821, ip=10.128.32.36) [2026-06-07 02:23:12,865 E 2596821 2596861] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4876: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning. +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) warnings.warn( # warn only once +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:15.648 | INFO  | skyrl_train.trainer:load_checkpoints:1715 - Successfully loaded policy checkpoint +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:15.648 | INFO  | skyrl_train.trainer:load_checkpoints:1731 - Successfully loaded complete checkpoint state from global_step_28 +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:15.648 | INFO  | skyrl_train.fully_async_trainer:_train_loop:512 - Resumed training from global_step 28 +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:15.663 | INFO  | skyrl_train.utils.data_tracker:load_state:97 - Loaded data tracker state: epoch=0, consumed_in_epoch=1792, total_consumed=1792 +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:15.664 | INFO  | skyrl_train.fully_async_trainer:_train_loop:510 - Finished: 'load_checkpoints', time cost: 19.31s +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:15.664 | INFO  | skyrl_train.fully_async_trainer:_train_loop:544 - Started: 'init_weight_sync_state' +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) No module named 'vllm._version' +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) 2026-06-07 02:23:19.911 | INFO  | logging:info:2216 - [weight-sync] Using master_addr=10.128.32.43, master_port=46453 +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank0]:[W607 02:23:19.180220203 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-43.jupiter.internal]:46453 (errno: 97 - Address family not supported by protocol). +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:20.003 | INFO  | skyrl_train.fully_async_trainer:_train_loop:544 - Finished: 'init_weight_sync_state', time cost: 4.34s +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:20.003 | ERROR  | skyrl_train.fully_async_trainer:train:493 - Train loop failed at global_step 28: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +(skyrl_entrypoint pid=294281) Traceback (most recent call last): +(skyrl_entrypoint pid=294281) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 491, in train +(skyrl_entrypoint pid=294281) await self._train_loop() +(skyrl_entrypoint pid=294281) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 545, in _train_loop +(skyrl_entrypoint pid=294281) self.init_weight_sync_state() +(skyrl_entrypoint pid=294281) File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 851, in init_weight_sync_state +(skyrl_entrypoint pid=294281) raise RuntimeError( +(skyrl_entrypoint pid=294281) RuntimeError: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py:470: UserWarning: No model update group to destroy +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) warnings.warn("No model update group to destroy") +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) 2026-06-07 02:23:19.910 | INFO  | logging:info:2216 - [weight-sync] get_node_ip_address()=10.128.32.43 [repeated 3x across cluster] +(FSDPPolicyWorkerBase pid=294462) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=294462) No module named 'vllm._version' [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=294462) from .version import __version__, __version_tuple__ # isort:skip [repeated 7x across cluster] +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:23:21.030 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:363 - Shutting down shared QueueOrchestrator... +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:23:21.030 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:365 - QueueOrchestrator shutdown complete +(RolloutCoordinator pid=2542476, ip=10.128.32.40) 2026-06-07 02:23:21.030 | INFO | examples.terminal_bench.rollout_coordinator:shutdown:258 - [RolloutCoordinator 1] shutdown complete +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:21.029 | INFO  | skyrl_train.inference_engines.inference_engine_client_http_endpoint:shutdown_server:203 - Server shut down after 2 seconds +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:21.029 | INFO  | skyrl_train.trainer:_guarded_sync:226 - HTTP endpoint shutdown complete +(skyrl_entrypoint pid=294281) 2026-06-07 02:23:21.033 | INFO  | skyrl_train.trainer:_guarded_async:215 - Generator shutdown complete +2026-06-07 02:23:21.090 | ERROR | __main__:main:134 - Training failed: ray::skyrl_entrypoint() (pid=294281, ip=10.128.32.33) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 105, in skyrl_entrypoint + exp.run() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/entrypoints/main_base.py", line 483, in run + asyncio.run(trainer.train()) + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 195, in run + return runner.run(main) + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 118, in run + return self._loop.run_until_complete(task) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 491, in train + await self._train_loop() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 545, in _train_loop + self.init_weight_sync_state() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 851, in init_weight_sync_state + raise RuntimeError( +RuntimeError: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +2026-06-07 02:23:21.091 | INFO | __main__:main:137 - Shutting down Ray on head node... +(AsyncVLLMInferenceEngine pid=2958820, ip=10.128.32.39) (EngineCore_DP0 pid=2959054) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py:470: UserWarning: No model update group to destroy [repeated 27x across cluster] +(AsyncVLLMInferenceEngine pid=2958820, ip=10.128.32.39) (EngineCore_DP0 pid=2959054) warnings.warn("No model update group to destroy") [repeated 27x across cluster] +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:23:21.030 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:363 - Shutting down shared QueueOrchestrator... [repeated 3x across cluster] +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:23:21.030 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:365 - QueueOrchestrator shutdown complete [repeated 3x across cluster] +(RolloutCoordinator pid=987693, ip=10.128.32.37) 2026-06-07 02:23:21.030 | INFO | examples.terminal_bench.rollout_coordinator:shutdown:258 - [RolloutCoordinator 3] shutdown complete [repeated 3x across cluster] +(skyrl_entrypoint pid=294281) [fd-monitor] Started monitoring (every 120s) +(skyrl_entrypoint pid=294281) [fd-monitor] [02:21:02] OK: 50 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=294281) [fd-monitor] [02:21:02] OK: RSS 1.38 GiB | node mem 129.1/858.0 GiB used (15.0%), avail 728.9 GiB +(skyrl_entrypoint pid=294281) ⚙️ Running in WANDB offline mode +(skyrl_entrypoint pid=294281) INFO 06-07 02:21:22 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(pid=987079, ip=10.128.32.37) INFO 06-07 02:21:39 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) WARNING 06-07 02:21:43 [arg_utils.py:1256] The global random seed is set to 42. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) INFO 06-07 02:21:43 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) INFO 06-07 02:21:43 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) INFO 06-07 02:21:43 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) INFO 06-07 02:21:43 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) INFO 06-07 02:21:43 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) WARNING 06-07 02:21:43 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) INFO 06-07 02:21:43 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) WARNING 06-07 02:21:44 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) WARNING 06-07 02:21:44 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(pid=987212, ip=10.128.32.37) INFO 06-07 02:21:44 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) WARNING 06-07 02:21:48 [arg_utils.py:1256] The global random seed is set to 56. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) WARNING 06-07 02:21:48 [arg_utils.py:1256] The global random seed is set to 55. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) INFO 06-07 02:21:48 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) INFO 06-07 02:21:48 [model.py:1549] Using max model len 32768 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) INFO 06-07 02:21:48 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) INFO 06-07 02:21:48 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) INFO 06-07 02:21:48 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) WARNING 06-07 02:21:48 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) INFO 06-07 02:21:48 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) WARNING 06-07 02:21:48 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) WARNING 06-07 02:21:48 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) WARNING 06-07 02:21:49 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) WARNING 06-07 02:21:49 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:48 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:21:51 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=42, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) WARNING 06-07 02:21:53 [arg_utils.py:1256] The global random seed is set to 61. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:53 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:53 [model.py:1549] Using max model len 32768 [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:53 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:53 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:53 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) WARNING 06-07 02:21:53 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) INFO 06-07 02:21:53 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) WARNING 06-07 02:21:53 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=3079064, ip=10.128.32.42) WARNING 06-07 02:21:53 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 16x across cluster] +(pid=2626785, ip=10.128.32.46) INFO 06-07 02:21:55 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:21:56 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:21:56 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.37:41139 backend=nccl +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:21:56 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:21:57 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=56, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:21:57 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:21:58 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) WARNING 06-07 02:21:58 [arg_utils.py:1256] The global random seed is set to 69. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) INFO 06-07 02:21:58 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) INFO 06-07 02:21:58 [model.py:1549] Using max model len 32768 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) INFO 06-07 02:21:58 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) INFO 06-07 02:21:58 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) INFO 06-07 02:21:58 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) WARNING 06-07 02:21:58 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626657, ip=10.128.32.46) INFO 06-07 02:21:58 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2626529, ip=10.128.32.46) WARNING 06-07 02:21:59 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2626529, ip=10.128.32.46) WARNING 06-07 02:21:59 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 9x across cluster] +(pid=2662729, ip=10.128.32.43) ⚙️ Running in WANDB offline mode +(pid=2578180, ip=10.128.32.41) INFO 06-07 02:22:00 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 26x across cluster] +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) INFO 06-07 02:22:01 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) INFO 06-07 02:22:01 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.37:50831 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) INFO 06-07 02:22:01 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2669550, ip=10.128.32.38) (EngineCore_DP0 pid=2669785) INFO 06-07 02:22:01 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=51, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 14x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) INFO 06-07 02:22:02 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) INFO 06-07 02:22:02 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) WARNING 06-07 02:22:02 [arg_utils.py:1256] The global random seed is set to 84. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) INFO 06-07 02:22:02 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) INFO 06-07 02:22:02 [model.py:1549] Using max model len 32768 [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) INFO 06-07 02:22:02 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) INFO 06-07 02:22:02 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) INFO 06-07 02:22:02 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) WARNING 06-07 02:22:02 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) INFO 06-07 02:22:02 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) WARNING 06-07 02:22:03 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) WARNING 06-07 02:22:03 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2704294, ip=10.128.32.34) INFO 06-07 02:22:05 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=3079063, ip=10.128.32.42) (EngineCore_DP0 pid=3079292) INFO 06-07 02:22:06 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2596339, ip=10.128.32.36) (EngineCore_DP0 pid=2596566) INFO 06-07 02:22:06 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.36:37763 backend=nccl [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2596339, ip=10.128.32.36) (EngineCore_DP0 pid=2596566) INFO 06-07 02:22:06 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2704294, ip=10.128.32.34) (EngineCore_DP0 pid=2704539) INFO 06-07 02:22:05 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=64, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2596340, ip=10.128.32.36) (EngineCore_DP0 pid=2596570) INFO 06-07 02:22:07 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 9x across cluster] +(pid=2662809, ip=10.128.32.43) ⚙️ Running in WANDB offline mode +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:22:08 [default_loader.py:293] Loading weights took 9.29 seconds +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) (EngineCore_DP0 pid=987452) INFO 06-07 02:22:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 6.143796 seconds +(AsyncVLLMInferenceEngine pid=2596340, ip=10.128.32.36) (EngineCore_DP0 pid=2596570) INFO 06-07 02:22:08 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) WARNING 06-07 02:22:05 [arg_utils.py:1256] The global random seed is set to 89. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) INFO 06-07 02:22:06 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) INFO 06-07 02:22:06 [model.py:1549] Using max model len 32768 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) INFO 06-07 02:22:06 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) INFO 06-07 02:22:06 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) INFO 06-07 02:22:06 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) WARNING 06-07 02:22:06 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) INFO 06-07 02:22:06 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) WARNING 06-07 02:22:06 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) WARNING 06-07 02:22:06 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2975429, ip=10.128.32.44) INFO 06-07 02:22:10 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) INFO 06-07 02:22:10 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:22:11 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:22:11 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:22:11 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) INFO 06-07 02:22:10 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.45:48431 backend=nccl [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) INFO 06-07 02:22:10 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:22:12 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) INFO 06-07 02:22:12 [core.py:278] init engine (profile, create kv cache, warmup model) took 3.50 seconds +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) INFO 06-07 02:22:12 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=84, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) INFO 06-07 02:22:11 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=987079, ip=10.128.32.37) (EngineCore_DP0 pid=987418) WARNING 06-07 02:22:12 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=987212, ip=10.128.32.37) WARNING 06-07 02:22:12 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(pid=294462) ⚙️ Running in WANDB offline mode [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:08 [default_loader.py:293] Loading weights took 6.05 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 6.835432 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2796149, ip=10.128.32.45) (EngineCore_DP0 pid=2796391) INFO 06-07 02:22:13 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:12 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:12 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) WARNING 06-07 02:22:12 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] EngineCore failed to start. +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 996, in run_engine_core +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 740, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] super().__init__( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 106, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] self.model_executor = executor_class(vllm_config) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/executor/abstract.py", line 103, in __init__ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] self._init_executor() +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/executor/uniproc_executor.py", line 47, in _init_executor +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] self.driver_worker.init_device() +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/worker/worker_base.py", line 332, in init_device +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] self.worker.init_device() # type: ignore +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/worker/gpu_worker.py", line 232, in init_device +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] init_worker_distributed_environment( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/worker/gpu_worker.py", line 1040, in init_worker_distributed_environment +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] init_distributed_environment( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/distributed/parallel_state.py", line 1256, in init_distributed_environment +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] torch.distributed.init_process_group( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] return func(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 95, in wrapper +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] func_return = func(*args, **kwargs) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py", line 1762, in init_process_group +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] store, rank, world_size = next(rendezvous_iterator) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 230, in _tcp_rendezvous_handler +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] store = _create_c10d_store( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 198, in _create_c10d_store +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] return TCPStore( +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] ^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] torch.distributed.DistNetworkError: The server socket has failed to listen on any local network address. port: 44571, useIpv6: false, code: -98, name: EADDRINUSE, message: address already in use +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] Exception raised from makeWithPort at /pytorch/torch/csrc/distributed/c10d/TCPStoreLibUvBackend.cpp:307 (most recent call first): +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0xb0 (0x40006f4ac700 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libc10.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #1: + 0x5f29220 (0x40004f649220 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #2: + 0x5f4326c (0x40004f66326c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #3: + 0x5f49074 (0x40004f669074 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #4: + 0x5f49138 (0x40004f669138 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #5: + 0x5f2ccc4 (0x40004f64ccc4 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #6: c10d::TCPStore::TCPStore(std::__cxx11::basic_string, std::allocator >, c10d::TCPStoreOptions const&) + 0x104 (0x40004f651564 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #7: + 0x109a094 (0x4000493fa094 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #8: + 0x113236c (0x40004949236c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #9: + 0x5d6d60 (0x400048936d60 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #10: + 0x1b7a38 (0xaaaab8697a38 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #11: _PyObject_MakeTpCall + 0x98 (0xaaaab8645db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #12: + 0x169f50 (0xaaaab8649f50 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #13: + 0x1682e4 (0xaaaab86482e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #14: + 0x1e0ce8 (0xaaaab86c0ce8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #15: + 0x1d7ddc (0xaaaab86b7ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #16: + 0x646b0c (0x4000489a6b0c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #17: _PyObject_MakeTpCall + 0x98 (0xaaaab8645db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #18: _PyEval_EvalFrameDefault + 0x280c (0xaaaab874ae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #19: + 0x1808c0 (0xaaaab86608c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #20: + 0x182bf8 (0xaaaab8662bf8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #21: + 0x25fd30 (0xaaaab873fd30 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #22: + 0x1b7d20 (0xaaaab8697d20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #23: PyObject_Vectorcall + 0x54 (0xaaaab86460e4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #24: _PyEval_EvalFrameDefault + 0x280c (0xaaaab874ae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #25: _PyObject_FastCallDictTstate + 0x80 (0xaaaab8647f20 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #26: _PyObject_Call_Prepend + 0x140 (0xaaaab86481ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #27: + 0x1e0d80 (0xaaaab86c0d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #28: + 0x1d7ddc (0xaaaab86b7ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #29: _PyObject_MakeTpCall + 0x98 (0xaaaab8645db8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #30: _PyEval_EvalFrameDefault + 0x280c (0xaaaab874ae54 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #31: _PyObject_FastCallDictTstate + 0x10c (0xaaaab8647fac in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #32: _PyObject_Call_Prepend + 0x140 (0xaaaab86481ec in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #33: + 0x1e0d80 (0xaaaab86c0d80 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #34: + 0x1d7ddc (0xaaaab86b7ddc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #35: _PyObject_Call + 0x68 (0xaaaab8648488 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #36: _PyEval_EvalFrameDefault + 0x52ac (0xaaaab874d8f4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #37: PyEval_EvalCode + 0xb4 (0xaaaab8752eb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #38: + 0x2ccdcc (0xaaaab87acdcc in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #39: + 0x2ccef4 (0xaaaab87acef4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #40: PyRun_StringFlags + 0x90 (0xaaaab87b1050 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #41: PyRun_SimpleStringFlags + 0x58 (0xaaaab87b10f8 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #42: Py_RunMain + 0x2c8 (0xaaaab87d9190 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #43: Py_BytesMain + 0x64 (0xaaaab87d9fb4 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #44: + 0x27540 (0x4000379a7540 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #45: __libc_start_main + 0x98 (0x4000379a7618 in /lib64/libc.so.6) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] frame #46: + 0x10e0c0 (0xaaaab85ee0c0 in VLLM::EngineCore) +(AsyncVLLMInferenceEngine pid=2958691, ip=10.128.32.39) (EngineCore_DP0 pid=2959070) ERROR 06-07 02:22:15 [core.py:1006] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) INFO 06-07 02:22:15 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) WARNING 06-07 02:22:16 [arg_utils.py:1256] The global random seed is set to 77. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) INFO 06-07 02:22:16 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) INFO 06-07 02:22:16 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) INFO 06-07 02:22:16 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) INFO 06-07 02:22:16 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) WARNING 06-07 02:22:16 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) INFO 06-07 02:22:16 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:11 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:11 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:11 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) INFO 06-07 02:22:16 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.44:41207 backend=nccl [repeated 18x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) INFO 06-07 02:22:16 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) WARNING 06-07 02:22:16 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:12 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) INFO 06-07 02:22:12 [core.py:278] init engine (profile, create kv cache, warmup model) took 3.49 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578050, ip=10.128.32.41) (EngineCore_DP0 pid=2578406) INFO 06-07 02:22:16 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=86, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) INFO 06-07 02:22:16 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=987213, ip=10.128.32.37) (EngineCore_DP0 pid=987447) WARNING 06-07 02:22:12 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=987214, ip=10.128.32.37) WARNING 06-07 02:22:12 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2796149, ip=10.128.32.45) (EngineCore_DP0 pid=2796391) INFO 06-07 02:22:18 [default_loader.py:293] Loading weights took 4.18 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2796149, ip=10.128.32.45) (EngineCore_DP0 pid=2796391) INFO 06-07 02:22:18 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 4.518817 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2975565, ip=10.128.32.44) (EngineCore_DP0 pid=2975804) INFO 06-07 02:22:17 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) INFO 06-07 02:22:19 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) INFO 06-07 02:22:19 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2796150, ip=10.128.32.45) (EngineCore_DP0 pid=2796384) WARNING 06-07 02:22:19 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2578050, ip=10.128.32.41) INFO 06-07 02:22:15 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) WARNING 06-07 02:22:17 [arg_utils.py:1256] The global random seed is set to 76. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) INFO 06-07 02:22:17 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) INFO 06-07 02:22:17 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) INFO 06-07 02:22:17 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) INFO 06-07 02:22:17 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) WARNING 06-07 02:22:17 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:20 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2796149, ip=10.128.32.45) (EngineCore_DP0 pid=2796391) INFO 06-07 02:22:20 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2796149, ip=10.128.32.45) (EngineCore_DP0 pid=2796391) INFO 06-07 02:22:20 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2796149, ip=10.128.32.45) (EngineCore_DP0 pid=2796391) INFO 06-07 02:22:20 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:20 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.41:43419 backend=nccl [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:20 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) WARNING 06-07 02:22:17 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) INFO 06-07 02:22:22 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) (EngineCore_DP0 pid=2542230) INFO 06-07 02:22:22 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.82 seconds [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:21 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2542123, ip=10.128.32.40) (EngineCore_DP0 pid=2542234) WARNING 06-07 02:22:22 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2541854, ip=10.128.32.40) WARNING 06-07 02:22:23 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) INFO 06-07 02:22:23 [default_loader.py:293] Loading weights took 13.40 seconds [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2669422, ip=10.128.32.38) (EngineCore_DP0 pid=2669802) INFO 06-07 02:22:23 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.574883 seconds [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:21 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 4x across cluster] +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) NCCL version 2.27.7+cuda13.0 +(AsyncVLLMInferenceEngine pid=2596338, ip=10.128.32.36) (EngineCore_DP0 pid=2596579) INFO 06-07 02:22:25 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2596338, ip=10.128.32.36) (EngineCore_DP0 pid=2596579) INFO 06-07 02:22:25 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2596338, ip=10.128.32.36) (EngineCore_DP0 pid=2596579) WARNING 06-07 02:22:25 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) (EngineCore_DP0 pid=2669786) INFO 06-07 02:22:26 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) (EngineCore_DP0 pid=2669786) INFO 06-07 02:22:26 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) (EngineCore_DP0 pid=2669786) INFO 06-07 02:22:26 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) (EngineCore_DP0 pid=2669786) INFO 06-07 02:22:26 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) INFO 06-07 02:22:26 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.80 seconds [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) WARNING 06-07 02:22:27 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) INFO 06-07 02:22:28 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2669551, ip=10.128.32.38) WARNING 06-07 02:22:27 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:28 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=76, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) INFO 06-07 02:22:28 [default_loader.py:293] Loading weights took 13.02 seconds [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2704294, ip=10.128.32.34) (EngineCore_DP0 pid=2704539) INFO 06-07 02:22:28 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.076531 seconds [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) INFO 06-07 02:22:27 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) INFO 06-07 02:22:27 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2669552, ip=10.128.32.38) (EngineCore_DP0 pid=2669784) WARNING 06-07 02:22:27 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 11x across cluster] +(RolloutCoordinator pid=2669976, ip=10.128.32.38) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=2669976, ip=10.128.32.38) [fd-monitor] [02:22:32] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=2669976, ip=10.128.32.38) [fd-monitor] [02:22:32] OK: RSS 0.78 GiB | node mem 385.3/858.0 GiB used (44.9%), avail 472.7 GiB +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) INFO 06-07 02:22:31 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) INFO 06-07 02:22:31 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) INFO 06-07 02:22:31 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) INFO 06-07 02:22:31 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) INFO 06-07 02:22:31 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.82 seconds [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) (EngineCore_DP0 pid=2626905) WARNING 06-07 02:22:32 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) INFO 06-07 02:22:28 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2626785, ip=10.128.32.46) WARNING 06-07 02:22:32 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:28 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=74, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) (EngineCore_DP0 pid=2975787) INFO 06-07 02:22:30 [default_loader.py:293] Loading weights took 13.13 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) (EngineCore_DP0 pid=2975787) INFO 06-07 02:22:30 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.203295 seconds [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:34 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:35 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.35:33029 backend=nccl +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:35 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2975564, ip=10.128.32.44) (EngineCore_DP0 pid=2975800) INFO 06-07 02:22:34 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2975564, ip=10.128.32.44) (EngineCore_DP0 pid=2975800) INFO 06-07 02:22:34 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=2975564, ip=10.128.32.44) (EngineCore_DP0 pid=2975800) WARNING 06-07 02:22:34 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:35 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:36 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) (EngineCore_DP0 pid=2578420) INFO 06-07 02:22:37 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) (EngineCore_DP0 pid=2578420) INFO 06-07 02:22:37 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2578179, ip=10.128.32.41) (EngineCore_DP0 pid=2578420) INFO 06-07 02:22:37 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2975564, ip=10.128.32.44) (EngineCore_DP0 pid=2975800) INFO 06-07 02:22:33 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) (EngineCore_DP0 pid=2975787) INFO 06-07 02:22:33 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.80 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578050, ip=10.128.32.41) (EngineCore_DP0 pid=2578406) WARNING 06-07 02:22:38 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2975563, ip=10.128.32.44) WARNING 06-07 02:22:34 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 4x across cluster] +(RolloutCoordinator pid=2542476, ip=10.128.32.40) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=2542476, ip=10.128.32.40) [fd-monitor] [02:22:39] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=2542476, ip=10.128.32.40) [fd-monitor] [02:22:39] OK: RSS 0.78 GiB | node mem 406.2/858.0 GiB used (47.3%), avail 451.7 GiB +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:34 [default_loader.py:293] Loading weights took 12.85 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:35 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 13.617507 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:35 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:35 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.35:40633 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:35 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:38 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:38 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) WARNING 06-07 02:22:38 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:35 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:36 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:37 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:37 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:37 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:38 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) INFO 06-07 02:22:38 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.83 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) (EngineCore_DP0 pid=2578416) WARNING 06-07 02:22:38 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2578180, ip=10.128.32.41) WARNING 06-07 02:22:38 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 4x across cluster] +(RolloutCoordinator pid=2596821, ip=10.128.32.36) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=2596821, ip=10.128.32.36) [fd-monitor] [02:22:48] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=2596821, ip=10.128.32.36) [fd-monitor] [02:22:48] OK: RSS 0.78 GiB | node mem 394.8/858.0 GiB used (46.0%), avail 463.2 GiB +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:50 [default_loader.py:293] Loading weights took 13.45 seconds +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) (EngineCore_DP0 pid=864312) INFO 06-07 02:22:50 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.577902 seconds +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:53 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:53 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:53 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:53 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:53 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:53 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:53 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:53 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.72 seconds +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) WARNING 06-07 02:22:54 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:54 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) WARNING 06-07 02:22:54 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=864077, ip=10.128.32.35) (EngineCore_DP0 pid=864319) INFO 06-07 02:22:54 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=863950, ip=10.128.32.35) WARNING 06-07 02:22:54 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(RolloutCoordinator pid=987693, ip=10.128.32.37) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=987693, ip=10.128.32.37) [fd-monitor] [02:22:56] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=987693, ip=10.128.32.37) [fd-monitor] [02:22:56] OK: RSS 0.78 GiB | node mem 390.4/858.0 GiB used (45.5%), avail 467.6 GiB +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:50 [default_loader.py:293] Loading weights took 13.45 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:50 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.715511 seconds [repeated 3x across cluster] +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Loading model from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy/model_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Loading extra_state from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy/extra_state_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Loading optim from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy/optim_world_size_8_rank_0.pt +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:53 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:53 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:53 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:53 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) (EngineCore_DP0 pid=864324) INFO 06-07 02:22:53 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.83 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) WARNING 06-07 02:22:54 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:54 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) WARNING 06-07 02:22:54 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864079, ip=10.128.32.35) (EngineCore_DP0 pid=864313) INFO 06-07 02:22:54 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=864078, ip=10.128.32.35) WARNING 06-07 02:22:54 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(skyrl_entrypoint pid=294281) [fd-monitor] [02:23:02] OK: 102 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=294281) [fd-monitor] [02:23:02] OK: RSS 1.64 GiB | node mem 233.1/858.0 GiB used (27.2%), avail 624.9 GiB +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Successfully loaded model state dict +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Successfully loaded optimizer state +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Successfully loaded scheduler state +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) [rank-0]: Checkpoint loaded successfully from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_28/policy +(FSDPPolicyWorkerBase pid=2662729, ip=10.128.32.43) INFO 06-07 02:23:18 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(FSDPPolicyWorkerBase pid=294462) INFO 06-07 02:23:19 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 7x across cluster] +Error executing job with overrides: ['+terminal_bench_config=terminal_bench', 'trainer.strategy=fsdp2', 'trainer.algorithm.advantage_estimator=rloo_n', 'trainer.algorithm.use_kl_loss=false', 'trainer.algorithm.kl_loss_coef=0.0', 'trainer.algorithm.eps_clip_low=0.2', 'trainer.algorithm.eps_clip_high=0.05', 'trainer.algorithm.loss_reduction=sequence_mean', 'trainer.epochs=2', 'trainer.max_steps=80', 'trainer.update_epochs_per_batch=1', 'trainer.train_batch_size=64', 'trainer.policy_mini_batch_size=64', 'trainer.eval_batch_size=64', 'trainer.micro_forward_batch_size_per_gpu=4', 'trainer.micro_train_batch_size_per_gpu=1', 'trainer.max_prompt_length=999999', 'trainer.eval_interval=999999', 'trainer.eval_before_train=false', 'trainer.ckpt_interval=2', 'trainer.resume_mode=latest', 'trainer.hf_save_interval=5', '++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0', '++trainer.hf_hub_private=false', '++trainer.hf_hub_revision=main', '++trainer.enable_db_registration=false', 'trainer.project_name=OpenThoughts-Agent', 'trainer.log_level=INFO', 'trainer.tracker_commit_each_step=true', 'trainer.logger=console', 'trainer.run_name=ablation-pymethods2test-seqmean-arm0', 'trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints', 'trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports', 'trainer.policy.optimizer_config.lr=8e-6', 'trainer.policy.optimizer_config.weight_decay=0.0', 'trainer.policy.optimizer_config.adam_betas=[0.9,0.999]', 'trainer.policy.optimizer_config.max_grad_norm=0.9', 'trainer.policy.fsdp_config.cpu_offload=false', 'trainer.policy.fsdp_config.reshard_after_forward=true', 'trainer.policy.fsdp_config.fsdp_size=4', 'trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'trainer.ref.fsdp_config.cpu_offload=false', 'trainer.ref.fsdp_config.reshard_after_forward=true', 'trainer.ref.fsdp_config.fsdp_size=4', 'trainer.placement.colocate_all=false', 'trainer.placement.policy_num_nodes=2', 'trainer.placement.ref_num_nodes=2', 'trainer.placement.policy_num_gpus_per_node=4', 'trainer.placement.ref_num_gpus_per_node=4', 'trainer.fully_async.max_staleness_steps=16', 'trainer.fully_async.num_parallel_generation_workers=338', 'generator.backend=vllm', 'generator.timeout_multiplier=1.0', 'generator.model_dtype=bfloat16', 'generator.inference_engine_tensor_parallel_size=1', 'generator.num_inference_engines=48', 'generator.n_samples_per_prompt=8', 'generator.eval_n_samples_per_prompt=8', 'generator.gpu_memory_utilization=0.75', 'generator.max_num_seqs=24', 'generator.max_num_batched_tokens=65536', 'generator.enable_prefix_caching=true', 'generator.enable_chunked_prefill=true', 'generator.run_engines_locally=true', 'generator.weight_sync_backend=nccl', 'generator.async_engine=true', 'generator.batched=false', 'generator.enable_http_endpoint=true', 'generator.enable_ray_prometheus_stats=false', 'generator.vllm_stats_interval=1', 'generator.append_eos_token_after_stop_str_in_multi_turn=true', 'generator.max_turns=999999', 'generator.sampling_params.max_generate_length=4096', 'generator.sampling_params.temperature=0.7', 'generator.sampling_params.top_p=0.95', 'generator.sampling_params.top_k=20', '++generator.engine_init_kwargs.max_model_len=32768', '++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2', '++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"]', 'data.val_data=[]', '+terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs', '+terminal_bench_config.harbor.name=terminus-2', '+terminal_bench_config.harbor.max_episodes=999999', '+terminal_bench_config.harbor.enable_summarize=false', '+terminal_bench_config.harbor.store_all_messages=true', '+terminal_bench_config.harbor.trajectory_config.raw_content=true', '+terminal_bench_config.harbor.enable_episode_logging=false', '+terminal_bench_config.harbor.record_terminal_session=false', '+terminal_bench_config.harbor.enable_pane_logging=false', '+terminal_bench_config.harbor.strict_json_parser=true', '+terminal_bench_config.harbor.interleaved_thinking=true', '+terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true', '+terminal_bench_config.harbor.override_timeout_sec=900', '+terminal_bench_config.harbor.override_cpus=1', '+terminal_bench_config.harbor.override_memory_mb=2048', '+terminal_bench_config.harbor.override_storage_mb=2048', '+terminal_bench_config.harbor.auto_snapshot=true', '+terminal_bench_config.harbor.verifier_override_timeout_sec=120', '+terminal_bench_config.harbor.max_retries=3', '+terminal_bench_config.harbor.min_wait_sec=60.0', '+terminal_bench_config.harbor.max_wait_sec=600.0', '+terminal_bench_config.harbor.wait_multiplier=2.0', '+terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"]', '+terminal_bench_config.harbor.n_concurrent_trials=675', '+terminal_bench_config.harbor.log_level=INFO', '+terminal_bench_config.harbor.enable_reward_shaping=false', '+terminal_bench_config.harbor.enable_error_classification=true', '+terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"]', '+terminal_bench_config.harbor.default_error_treatment=zero', '+terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"]', '+terminal_bench_config.harbor.zero_exceptions=[]', '+terminal_bench_config.model_info.max_input_tokens=32000', '+terminal_bench_config.model_info.max_output_tokens=4096', '+terminal_bench_config.archiving.enabled=false', '+terminal_bench_config.trace_upload.enabled=true', '+terminal_bench_config.trace_upload.repo_org=DCAgent', '+terminal_bench_config.trace_upload.episodes=last', '+terminal_bench_config.trace_upload.dataset_type=SFT', '+terminal_bench_config.trace_upload.cleanup=true'] +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 142, in + main() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/main.py", line 94, in decorated_main + _run_hydra( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra + _run_app( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 457, in _run_app + run_and_report( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 223, in run_and_report + raise ex + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 220, in run_and_report + return func() + ^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 458, in + lambda: hydra.run( + ^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/hydra.py", line 132, in run + _ = ret.return_value + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 260, in return_value + raise self._return_value + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 186, in run_job + ret.return_value = task_function(task_cfg) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 132, in main + ray.get(skyrl_entrypoint.remote(cfg)) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 2967, in get + values, debugger_breakpoint = worker.get_objects( + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 1015, in get_objects + raise value.as_instanceof_cause() +ray.exceptions.RayTaskError(RuntimeError): ray::skyrl_entrypoint() (pid=294281, ip=10.128.32.33) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 105, in skyrl_entrypoint + exp.run() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/entrypoints/main_base.py", line 483, in run + asyncio.run(trainer.train()) + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 195, in run + return runner.run(main) + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/runners.py", line 118, in run + return self._loop.run_until_complete(task) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 491, in train + await self._train_loop() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/fully_async_trainer.py", line 545, in _train_loop + self.init_weight_sync_state() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 851, in init_weight_sync_state + raise RuntimeError( +RuntimeError: init_weight_sync_state failed at Ray boundary: RayTaskError(RuntimeError)(RuntimeError('tp_size() failed at Ray boundary: ActorDiedError(RayTaskError(\'__init__\', \'Traceback (most recent call last):\\n File "python/ray/_raylet.pyx", line 1722, in ray._raylet.execute_task\\n File "python/ray/_raylet.pyx", line 1659, in ray._raylet.execute_task.function_executor\\n File "python/ray/_raylet.pyx", line 4342, in ray._raylet.CoreWorker.run_async_func_or_coro_in_event_loop\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result\\n return self.__get_result()\\n ^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result\\n raise self._exception\\n File "python/ray/_raylet.pyx", line 4329, in async_func\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/async_compat.py", line 52, in wrapper\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/function_manager.py", line 693, in actor_method_executor\\n return method(__ray_actor, *args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1139, in __init__\\n super().__init__(*args, **kwargs)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 603, in __init__\\n self.llm = self._create_engine(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/util/tracing/tracing_helper.py", line 461, in _resume_span\\n return method(self, *_args, **_kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/inference_engines/vllm/vllm_engine.py", line 1216, in _create_engine\\n engine = vllm.AsyncLLMEngine.from_engine_args(engine_args, stat_loggers=stat_loggers)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 251, in from_engine_args\\n return cls(\\n ^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py", line 148, in __init__\\n self.engine_core = EngineCoreClient.make_async_mp_client(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 124, in make_async_mp_client\\n return AsyncMPClient(*client_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 835, in __init__\\n super().__init__(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 490, in __init__\\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/contextlib.py", line 144, in __exit__\\n next(self.gen)\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 936, in launch_core_engines\\n wait_for_engine_startup(\\n File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/v1/engine/utils.py", line 995, in wait_for_engine_startup\\n raise RuntimeError(\\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\\n\', RuntimeError(\'Engine core initialization failed. See root cause above. Failed core proc(s): {}\'), \'ray::AsyncVLLMInferenceEngine.__init__\', None, None))')) +Stopping Ray cluster... +Ray cluster stopped +[RLJobRunner] Crash detected (exit!=0) — preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/ray_logs BEFORE trace upload (so a wall-clock kill can't lose crash evidence)... + Collecting Ray logs from worker jpbo-041-34... + Collecting Ray logs from worker jpbo-041-35... + Collecting Ray logs from worker jpbo-041-36... + Collecting Ray logs from worker jpbo-041-37... + Collecting Ray logs from worker jpbo-041-38... + Collecting Ray logs from worker jpbo-041-39... + Collecting Ray logs from worker jpbo-041-40... + Collecting Ray logs from worker jpbo-041-41... + Collecting Ray logs from worker jpbo-041-42... + Collecting Ray logs from worker jpbo-041-43... + Collecting Ray logs from worker jpbo-041-44... + Collecting Ray logs from worker jpbo-041-45... + Collecting Ray logs from worker jpbo-041-46... +[RLJobRunner] Crash-time Ray log preservation complete. +[RLJobRunner] Launching trace upload (training exit code: 1): + repo_id: DCAgent/ablation-pymethods2test-seqmean-arm0 + job_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0 + episodes: last + log: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/logs/ablation-pymethods2test-seqmean-arm0_trace_upload.log +[RLJobRunner] Waiting for trace upload to complete... +[RLJobRunner] Trace upload failed with exit code 1. +Preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ray_logs/ + Collecting Ray logs from worker jpbo-041-34... + Collecting Ray logs from worker jpbo-041-35... + Collecting Ray logs from worker jpbo-041-36... + Collecting Ray logs from worker jpbo-041-37... + Collecting Ray logs from worker jpbo-041-38... + Collecting Ray logs from worker jpbo-041-39... + Collecting Ray logs from worker jpbo-041-40... + Collecting Ray logs from worker jpbo-041-41... + Collecting Ray logs from worker jpbo-041-42... + Collecting Ray logs from worker jpbo-041-43... + Collecting Ray logs from worker jpbo-041-44... + Collecting Ray logs from worker jpbo-041-45... + Collecting Ray logs from worker jpbo-041-46... +Ray log preservation complete diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630127.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630127.out new file mode 100644 index 0000000..e89b879 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630127.out @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acba1fb03567f31a572dea981ac325fe2b4df0ecb5e9b3c49722f70599c5311e +size 42935463 diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630128.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630128.out new file mode 100644 index 0000000..336c0bc --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630128.out @@ -0,0 +1,370 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Activating RL environment: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl +Python executable: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python path check: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +[ray] RAY_TMPDIR=/tmp/ray/ray_630128 +[triton_cache] Triton cache: /tmp/triton_cache_feuer1_630128 +[triton_cache] TorchInductor cache: /tmp/torchinductor_cache_feuer1_630128 +[proxy] ✓ Found proxychains binary at /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 +[proxy] Setting up SSH tunnel to jpbl-s01-01 +[proxy] SSH key: /e/home/jusers/feuer1/jupiter/.ssh/authorized_keys/id_ed25519_jsc +[proxy] Tunnel port: 7003 +[proxy] Node IP: 10.128.32.33 (workers will connect here) +[proxy] ✓ SSH tunnel started successfully +[proxy] ✓ Generated proxychains config at /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630128.conf +[proxy] - Internal traffic (10.x.x.x, 172.x.x.x, 169.254.x.x) → DIRECT +[proxy] - External traffic (internet) → PROXY via tunnel +[proxy] ✓ Daytona timeout settings configured +[proxy] Testing proxy connectivity... +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630128.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[proxy] ✓ Proxy connectivity test passed (huggingface.co reachable via wrapped binary) +[proxy] ⚠ Tunnel not accessible at 10.128.32.33:7003 (workers may fail) +[proxy] ✓ Proxy setup complete (using wrapped binary for Ray workers) +[container_runtime] Using cloud backend: daytona (no local container setup) +=== Universal RL Training Runner === +Config: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/configs/ablation-pymethods2test-seqmean-arm0_rl_config.json +Working directory: /e/scratch/jureap59/feuer1/OpenThoughts-Agent +Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python version: Python 3.12.12 +UV_USE_IO_URING: 0 +Proxy: DISABLED (direct internet or not configured) +======================================== +=== RLJobRunner: ablation-pymethods2test-seqmean-arm0 === +[wandb_utils] Fixing permissions on: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +[wandb_utils] WandB directory ready: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb + HF_TOKEN=****pDbg + HF_HUB_CACHE=/e/data1/datasets/playground/ot-baf/hf_hub + SUPABASE_URL=https://rpzmyuapoqilpghynmza.s... (direct Supabase config) +Environment configured: + TENSOR_PARALLEL_SIZE=1 + NUM_INFERENCE_ENGINES=56 + POLICY_NUM_NODES=14 + WANDB_DIR=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +Starting Ray cluster with 14 nodes, 4 GPUs/node +Cleaning up existing Ray instances... +=== Starting Ray Cluster === + Nodes: 14 + GPUs per node: 4 + CPUs per node: 288 + Head node: jpbo-041-33 (10.128.32.33) + Ray port: 6379 +============================ + Starting Ray head on jpbo-041-33 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_head_jpbo-041-33.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.33 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-33 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --head --node-ip-address=10.128.32.33 --port=6379 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray head on jpbo-041-33 + Starting Ray worker on jpbo-041-34 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-34.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.34 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-34 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.34 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 1 on jpbo-041-34 + Starting Ray worker on jpbo-041-35 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-35.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.35 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-35 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.35 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 2 on jpbo-041-35 + Starting Ray worker on jpbo-041-36 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-36.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.36 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-36 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.36 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 3 on jpbo-041-36 + Starting Ray worker on jpbo-041-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.37 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.37 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 4 on jpbo-041-37 + Starting Ray worker on jpbo-041-38 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-38.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.38 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-38 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.38 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 5 on jpbo-041-38 + Starting Ray worker on jpbo-041-39 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-39.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.39 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-39 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.39 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 6 on jpbo-041-39 + Starting Ray worker on jpbo-041-40 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-40.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.40 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-40 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.40 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 7 on jpbo-041-40 + Starting Ray worker on jpbo-041-41 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-41.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.41 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-41 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.41 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 8 on jpbo-041-41 + Starting Ray worker on jpbo-041-42 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-42.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.42 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-42 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.42 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 9 on jpbo-041-42 + Starting Ray worker on jpbo-041-43 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-43.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.43 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-43 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.43 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 10 on jpbo-041-43 + Starting Ray worker on jpbo-041-44 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-44.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.44 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-44 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.44 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 11 on jpbo-041-44 + Starting Ray worker on jpbo-041-45 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-45.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.45 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-45 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.45 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 12 on jpbo-041-45 + Starting Ray worker on jpbo-041-46 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-46.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.46 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-46 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.46 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 13 on jpbo-041-46 + Waiting for cluster (56 GPUs, 14 nodes)... +Connecting to Ray at 10.128.32.33:6379 (expecting 14 nodes, 56.0 GPUs) +Ray connection established, polling for resources... +[Ray wait] nodes=2/14 GPUs=8.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 1511604617216.0, 'accelerator_type:GH200': 2.0, 'CPU': 576.0, 'GPU': 8.0, 'object_store_memory': 85899345920.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0} +[Ray wait] nodes=2/14 GPUs=8.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 1511604617216.0, 'accelerator_type:GH200': 2.0, 'CPU': 576.0, 'GPU': 8.0, 'object_store_memory': 85899345920.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0} +[Ray wait] nodes=3/14 GPUs=12.0/56.0 resources={'GPU': 12.0, 'CPU': 864.0, 'accelerator_type:GH200': 3.0, 'memory': 2273135755264.0, 'node:10.128.32.44': 1.0, 'object_store_memory': 128849018880.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0} +[Ray wait] nodes=4/14 GPUs=16.0/56.0 resources={'node:10.128.32.44': 1.0, 'CPU': 1152.0, 'accelerator_type:GH200': 4.0, 'memory': 3033402310656.0, 'GPU': 16.0, 'object_store_memory': 171798691840.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0} +[Ray wait] nodes=4/14 GPUs=16.0/56.0 resources={'node:10.128.32.44': 1.0, 'memory': 3033402310656.0, 'accelerator_type:GH200': 4.0, 'CPU': 1152.0, 'GPU': 16.0, 'object_store_memory': 171798691840.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.43': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0} +[Ray wait] nodes=5/14 GPUs=20.0/56.0 resources={'GPU': 20.0, 'memory': 3809847214080.0, 'accelerator_type:GH200': 5.0, 'CPU': 1440.0, 'node:10.128.32.44': 1.0, 'object_store_memory': 214748364800.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0} +[Ray wait] nodes=6/14 GPUs=24.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 4562133647360.0, 'accelerator_type:GH200': 6.0, 'CPU': 1728.0, 'GPU': 24.0, 'object_store_memory': 257698037760.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0} +[Ray wait] nodes=7/14 GPUs=28.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2016.0, 'accelerator_type:GH200': 7.0, 'memory': 5307486961664.0, 'GPU': 28.0, 'object_store_memory': 300647710720.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=7/14 GPUs=28.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2016.0, 'accelerator_type:GH200': 7.0, 'memory': 5307486961664.0, 'GPU': 28.0, 'object_store_memory': 300647710720.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=8/14 GPUs=32.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2304.0, 'accelerator_type:GH200': 8.0, 'memory': 6067020955648.0, 'GPU': 32.0, 'object_store_memory': 343597383680.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=8/14 GPUs=32.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2304.0, 'accelerator_type:GH200': 8.0, 'memory': 6067020955648.0, 'GPU': 32.0, 'object_store_memory': 343597383680.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=8/14 GPUs=32.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2304.0, 'accelerator_type:GH200': 8.0, 'memory': 6067020955648.0, 'GPU': 32.0, 'object_store_memory': 343597383680.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=8/14 GPUs=32.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 6067020955648.0, 'accelerator_type:GH200': 8.0, 'CPU': 2304.0, 'GPU': 32.0, 'object_store_memory': 343597383680.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'memory': 7593839362048.0, 'accelerator_type:GH200': 10.0, 'CPU': 2880.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.33': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.32.43': 1.0, 'CPU': 2880.0, 'accelerator_type:GH200': 10.0, 'memory': 7593839362048.0, 'GPU': 40.0, 'object_store_memory': 429496729600.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.40': 1.0} + srun stderr: srun: error: _find_node_record: lookup failure for node "jpbo-123-29" +srun: error: _find_node_record: lookup failure for node "jpbo-123-30" +srun: error: _find_node_record: lookup failure for node "jpbo-123-31" +srun: error: _find_node_record: lookup failure for node "jpbo-123-32" +srun: error: _find_node_record: lookup failure for node "jpbo-123-33" +srun: error: _find_node_record: lookup failure for node "jpbo-123-34" +srun: error: _find_node_record: lookup failure for node "jpbo-123-35" +srun: error: _find_node_record: lookup failure for node "jpbo-123-36" +srun: error: _find_node_record: lookup failure for node "jpbo-123-37" +srun: error: _find_node_record: lookup failure for node "jpbo-123-38" +srun: error: _find_node_record: lookup failure for node "jpbo-123-39" +srun: error: _find_node_record: lookup failure for node "jpbo-123-40" +srun: error: _find_node_record: lookup failure for node "jpbo-123-41" +srun: error: _find_node_record: lookup failure for node "jpbo-123-42" +srun: error: _find_node_record: lookup failure for node "jpbo-123-43" +srun: error: _find_node_record: lookup failure for node "jpbo-123-44" +srun: error: _find_node_record: lookup failure for node "jpbo-123-45" +srun: error: _find_node_record: lookup failure for node "jpbo-123-46" +srun: error: _find_node_record: lookup failure for node "jpbo-123-47" +srun: error: _find_node_record: lookup failure for node "jpbo-123-48" +srun: error: _find_node_record: lookup failure for node "jpbo-124-01" +srun: error: _find_node_record: lookup failure for node "jpbo-124-02" +srun: error: _find_node_record: lookup failure for node "jpbo-124-03" +srun: error: _find_node_record: lookup failure for node "jpbo-124-04" +srun: error: _find_node_record: lookup failure for node "jpbo-124-05" +srun: error: _find_node_record: lookup failure for node "jpbo-124-06" +srun: error: _find_node_record: lookup failure for node "jpbo-124-07" +srun: error: _find_node_record: lookup failure for node "jpbo-124-08" +srun: error: _find_node_record: lookup failure for node "jpbo-124-09" +srun: error: _find_node_record: lookup failure for node "jpbo-124-10" +srun: error: _find_node_record: lookup failure for node "jpbo-124-11" +srun: error: _find_node_record: lookup failure for node "jpbo-124-12" +srun: error: _find_node_record: lookup failure for node "jpbo-124-13" +srun: error: _find_node_record: lookup failure for node "jpbo-124-14" +srun: error: _find_node_record: lookup failure for node "jpbo-124-15" +srun: error: _find_node_record: lookup failure for node "jpbo-124-16" +srun: error: _find_node_record: lookup failure for node "jpbo-124-17" +srun: error: _find_node_record: lookup failure for node "jpbo-124-18" +srun: error: _find_node_record: lookup failure for node "jpbo-124-19" +srun: error: _find_node_record: lookup failure for node "jpbo-124-20" +srun: error: _find_node_record: lookup failure for node "jpbo-124-21" +srun: error: _find_node_record: lookup failure for node "jpbo-124-22" +srun: error: _find_node_record: lookup failure for node "jpbo-124-23" +srun: error: _find_node_record: lookup failure for node "jpbo-124-24" +srun: error: _find_node_record: lookup failure for node "jpbo-124-25" +srun: error: _find_node_record: lookup failure for node "jpbo-124-26" +srun: error: _find_node_record: lookup failure for node "jpbo-124-27" +srun: error: _find_node_record: lookup failure for node "jpbo-124-28" +srun: error: _find_node_record: lookup failure for node "jpbo-124-29" +srun: error: _find_node_record: lookup failure for node "jpbo-124-30" +srun: error: _find_node_record: lookup failure for node "jpbo-124-31" +srun: error: _find_node_record: lookup failure for node "jpbo-124-32" +srun: error: _find_node_record: lookup failure for node "jpbo-124-33" +srun: error: _find_node_record: lookup failure for node "jpbo-124-34" +srun: error: _find_node_record: lookup failure for node "jpbo-124-35" +srun: error: _find_node_record: lookup failure for node "jpbo-124-36" +srun: error: _find_node_record: lookup failure for node "jpbo-124-37" +srun: error: _find_node_record: lookup failure for node "jpbo-124-38" +srun: error: _find_node_record: lookup failure for node "jpbo-124-39" +srun: error: _find_node_record: lookup failure for node "jpbo-124-40" +srun: error: _find_node_record: lookup failure for node "jpbo-124-41" +srun: error: _find_node_record: lookup failure for node "jpbo-124-42" +srun: error: _find_node_record: lookup failure for node "jpbo-124-43" +srun: error: _find_node_record: lookup failure for node "jpbo-124-44" +srun: error: _find_node_record: lookup failure for node "jpbo-124-45" +srun: error: _find_node_record: lookup failure for node "jpbo-124-46" +srun: error: _find_node_record: lookup failure for node "jpbo-124-47" +srun: error: _find_node_record: lookup failure for node "jpbo-124-48" +srun: error: _find_node_record: lookup failure for node "jpbo-125-01" +srun: error: _find_node_record: lookup failure for node "jpbo-125-02" +srun: error: _find_node_record: lookup failure for node "jpbo-125-03" +srun: error: _find_node_record: lookup failure for node "jpbo-125-04" +srun: error: _find_node_record: lookup failure for node "jpbo-125-05" +srun: error: _find_node_record: lookup failure for node "jpbo-125-06" +srun: error: _find_node_record: lookup failure for node "jpbo-125-07" +srun: error: _find_node_record: lookup failure for node "jpbo-125-08" +srun: error: _find_node_record: lookup failure for node "jpbo-125-09" +srun: error: _find_node_record: lookup failure for node "jpbo-125-10" +srun: error: _find_node_record: lookup failure for node "jpbo-125-11" +srun: error: _find_node_record: lookup failure for node "jpbo-125-12" +srun: error: _find_node_record: lookup failure for node "jpbo-125-13" +srun: error: _find_node_record: lookup failure for node "jpbo-125-14" +srun: error: _find_node_record: lookup failure for node "jpbo-125-15" +srun: error: _find_node_record: lookup failure for node "jpbo-125-16" +srun: error: _find_node_record: lookup failure for node "jpbo-125-17" +srun: error: _find_node_record: lookup failure for node "jpbo-125-18" +srun: error: _find_node_record: lookup failure for node "jpbo-125-19" +srun: error: _find_node_record: lookup failure for node "jpbo-125-20" +srun: error: _find_node_record: lookup failure for node "jpbo-125-21" +srun: error: _find_node_record: lookup failure for node "jpbo-125-22" +srun: error: _find_node_record: lookup failure for node "jpbo-125-23" +srun: error: _find_node_record: lookup failure for node "jpbo-125-24" +srun: error: _find_node_record: lookup failure for node "jpbo-125-25" +srun: error: _find_node_record: lookup failure for node "jpbo-125-26" +srun: error: _find_node_record: lookup failure for node "jpbo-125-27" +srun: error: _find_node_record: lookup failure for node "jpbo-125-28" +srun: error: _find_node_record: lookup failure for node "jpbo-125-29" +srun: error: _find_node_record: lookup failure for node "jpbo-125-30" +srun: error: _find_node_record: lookup failure for node "jpbo-125-31" +srun: error: _find_node_record: lookup failure for node "jpbo-125-32" +srun: error: _find_node_record: lookup failure for node "jpbo-125-33" +srun: error: _find_node_record: lookup failure for node "jpbo-125-34" +srun: error: _find_node_record: lookup failure for node "jpbo-125-35" +srun: error: _find_node_record: lookup failure for node "jpbo-125-36" +srun: error: _find_node_record: lookup failure for node "jpbo-125-37" +srun: error: _find_node_record: lookup failure for node "jpbo-125-38" +srun: error: _find_node_record: lookup failure for node "jpbo-125-39" +srun: error: _find_node_record: lookup failure for node "jpbo-125-40" +srun: error: _find_node_record: lookup failure for node "jpbo-125-41" +srun: error: _find_node_record: lookup failure for node "jpbo-125-42" +srun: error: _find_node_record: lookup failure for node "jpbo-125-43" +srun: error: _find_node_record: lookup failure for node "jpbo-125-44" +srun: error: _find_node_record: lookup failure for node "jpbo-125-45" +srun: error: _find_node_record: lookup failure for node "jpbo-125-46" +srun: error: _find_node_record: lookup failure for node "jpbo-125-47" +srun: error: _find_node_record: lookup failure for node "jpbo-125-48" +srun: warning: Invalid hostnames in switch configuration: jpbo-123-[29-48],jpbo-124-[01-48],jpbo-125-[01-48] +2026-06-07 14:35:10,656 INFO worker.py:1821 -- Connecting to existing Ray cluster at address: 10.128.32.33:6379... +2026-06-07 14:35:10,666 INFO worker.py:2007 -- Connected to Ray cluster. +/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0 + warnings.warn( +(raylet, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.43) [2026-06-07 14:35:49,687 E 2691736 2691736] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=352159) [2026-06-07 14:35:54,163 E 352159 352769] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=352184) [2026-06-07 14:36:00,280 E 352184 353748] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 18x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) +(raylet, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[2026-06-07 14:36:04,817 E 352114 352155] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.44) [2026-06-07 14:36:16,668 E 3005389 3005389] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=352328) [2026-06-07 14:36:04,815 E 352328 368299] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 269x across cluster] +(raylet, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.42) [2026-06-07 14:36:31,723 E 3108436 3108436] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.34) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.46) [2026-06-07 14:36:47,990 E 2656361 2656361] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.40) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 3x across cluster] +(raylet, ip=10.128.32.34) [2026-06-07 14:37:02,131 E 2733873 2733873] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.40) [2026-06-07 14:37:06,688 E 2571074 2571074] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.36) [2026-06-07 14:37:33,965 E 2625779 2625779] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.32.45) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 3x across cluster] +(raylet, ip=10.128.32.38) [2026-06-07 14:38:06,213 E 2699635 2699635] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.45) [2026-06-07 14:38:14,810 E 2825651 2825651] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +Traceback (most recent call last): + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/scripts/ray/wait_for_cluster.py", line 116, in + main() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/scripts/ray/wait_for_cluster.py", line 106, in main + wait_for_cluster( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/scripts/ray/wait_for_cluster.py", line 94, in wait_for_cluster + raise TimeoutError( +TimeoutError: Ray cluster did not reach desired resources within 600 seconds (nodes=10, gpus=40.0) +srun: error: jpbo-041-33: task 0: Exited with exit code 1 + +RL job failed: Ray cluster failed to start within 600s (last error: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs,TORCH_NCCL_TRACE_BUFFER_SIZE=10000,TORCH_FR_BUFFER_SIZE=10000,TORCH_NCCL_DESYNC_DEBUG=1,TORCH_NCCL_DEBUG_INFO_TEMP_FILE=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_PYNCCL_TRACE_BUFFER_SIZE=10000,VLLM_PYNCCL_TRACE_DUMP_DIR=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY=TORCH_NCCL_,TORCH_FR_,VLLM_PYNCCL_', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-041-33', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python scripts/ray/wait_for_cluster.py --address 10.128.32.33:6379 --expected-gpus 56 --expected-nodes 14 --timeout 600 --poll-interval 10']' returned non-zero exit status 1.) +Traceback (most recent call last): + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 651, in _wait_for_cluster + result = subprocess.run( + ^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/subprocess.py", line 571, in run + raise CalledProcessError(retcode, process.args, +subprocess.CalledProcessError: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs,TORCH_NCCL_TRACE_BUFFER_SIZE=10000,TORCH_FR_BUFFER_SIZE=10000,TORCH_NCCL_DESYNC_DEBUG=1,TORCH_NCCL_DEBUG_INFO_TEMP_FILE=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_PYNCCL_TRACE_BUFFER_SIZE=10000,VLLM_PYNCCL_TRACE_DUMP_DIR=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY=TORCH_NCCL_,TORCH_FR_,VLLM_PYNCCL_', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-041-33', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python scripts/ray/wait_for_cluster.py --address 10.128.32.33:6379 --expected-gpus 56 --expected-nodes 14 --timeout 600 --poll-interval 10']' returned non-zero exit status 1. + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/rl_launch_utils.py", line 1285, in run + training_exit_code = self._run_with_ray() + ^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/rl_launch_utils.py", line 1532, in _run_with_ray + with RayCluster.from_slurm(ray_cfg) as ray_cluster: + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 888, in __enter__ + self.start() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 377, in start + self._wait_for_cluster() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 679, in _wait_for_cluster + raise RuntimeError( +RuntimeError: Ray cluster failed to start within 600s (last error: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs,TORCH_NCCL_TRACE_BUFFER_SIZE=10000,TORCH_FR_BUFFER_SIZE=10000,TORCH_NCCL_DESYNC_DEBUG=1,TORCH_NCCL_DEBUG_INFO_TEMP_FILE=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_PYNCCL_TRACE_BUFFER_SIZE=10000,VLLM_PYNCCL_TRACE_DUMP_DIR=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY=TORCH_NCCL_,TORCH_FR_,VLLM_PYNCCL_', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-041-33', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python scripts/ray/wait_for_cluster.py --address 10.128.32.33:6379 --expected-gpus 56 --expected-nodes 14 --timeout 600 --poll-interval 10']' returned non-zero exit status 1.) +[RLJobRunner] Crash detected (exit!=0) — preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/ray_logs BEFORE trace upload (so a wall-clock kill can't lose crash evidence)... +[RLJobRunner] Crash-time Ray log preservation timed out (600s); continuing. +[RLJobRunner] Launching trace upload (training exit code: 1): + repo_id: DCAgent/ablation-pymethods2test-seqmean-arm0 + job_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0 + episodes: last + log: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/logs/ablation-pymethods2test-seqmean-arm0_trace_upload.log +[RLJobRunner] Waiting for trace upload to complete... +[RLJobRunner] Trace upload failed with exit code 1. +Preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ray_logs/ + Collecting Ray logs from worker jpbo-041-34... + Collecting Ray logs from worker jpbo-041-35... + Collecting Ray logs from worker jpbo-041-36... + Collecting Ray logs from worker jpbo-041-37... + Collecting Ray logs from worker jpbo-041-38... + Collecting Ray logs from worker jpbo-041-39... + Collecting Ray logs from worker jpbo-041-40... + Collecting Ray logs from worker jpbo-041-41... + Collecting Ray logs from worker jpbo-041-42... + Collecting Ray logs from worker jpbo-041-43... + Collecting Ray logs from worker jpbo-041-44... + Collecting Ray logs from worker jpbo-041-45... + Collecting Ray logs from worker jpbo-041-46... +Ray log preservation complete diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630129.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630129.out new file mode 100644 index 0000000..1dbc5ca --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630129.out @@ -0,0 +1,2054 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Activating RL environment: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl +Python executable: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python path check: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +[ray] RAY_TMPDIR=/tmp/ray/ray_630129 +[triton_cache] Triton cache: /tmp/triton_cache_feuer1_630129 +[triton_cache] TorchInductor cache: /tmp/torchinductor_cache_feuer1_630129 +[proxy] ✓ Found proxychains binary at /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 +[proxy] Setting up SSH tunnel to jpbl-s01-01 +[proxy] SSH key: /e/home/jusers/feuer1/jupiter/.ssh/authorized_keys/id_ed25519_jsc +[proxy] Tunnel port: 7003 +[proxy] Node IP: 10.128.32.33 (workers will connect here) +[proxy] ✓ SSH tunnel started successfully +[proxy] ✓ Generated proxychains config at /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630129.conf +[proxy] - Internal traffic (10.x.x.x, 172.x.x.x, 169.254.x.x) → DIRECT +[proxy] - External traffic (internet) → PROXY via tunnel +[proxy] ✓ Daytona timeout settings configured +[proxy] Testing proxy connectivity... +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630129.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[proxy] ✓ Proxy connectivity test passed (huggingface.co reachable via wrapped binary) +[proxy] ⚠ Tunnel not accessible at 10.128.32.33:7003 (workers may fail) +[proxy] ✓ Proxy setup complete (using wrapped binary for Ray workers) +[container_runtime] Using cloud backend: daytona (no local container setup) +=== Universal RL Training Runner === +Config: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/configs/ablation-pymethods2test-seqmean-arm0_rl_config.json +Working directory: /e/scratch/jureap59/feuer1/OpenThoughts-Agent +Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python version: Python 3.12.12 +UV_USE_IO_URING: 0 +Proxy: DISABLED (direct internet or not configured) +======================================== +=== RLJobRunner: ablation-pymethods2test-seqmean-arm0 === +[wandb_utils] Fixing permissions on: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +[wandb_utils] WandB directory ready: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb + HF_TOKEN=****pDbg + HF_HUB_CACHE=/e/data1/datasets/playground/ot-baf/hf_hub + SUPABASE_URL=https://rpzmyuapoqilpghynmza.s... (direct Supabase config) +Environment configured: + TENSOR_PARALLEL_SIZE=1 + NUM_INFERENCE_ENGINES=56 + POLICY_NUM_NODES=14 + WANDB_DIR=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/wandb +Starting Ray cluster with 14 nodes, 4 GPUs/node +Cleaning up existing Ray instances... +=== Starting Ray Cluster === + Nodes: 14 + GPUs per node: 4 + CPUs per node: 288 + Head node: jpbo-041-33 (10.128.32.33) + Ray port: 6379 +============================ + Starting Ray head on jpbo-041-33 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_head_jpbo-041-33.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.33 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-33 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --head --node-ip-address=10.128.32.33 --port=6379 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray head on jpbo-041-33 + Starting Ray worker on jpbo-041-34 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-34.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.34 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-34 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.34 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 1 on jpbo-041-34 + Starting Ray worker on jpbo-041-35 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-35.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.35 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-35 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.35 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 2 on jpbo-041-35 + Starting Ray worker on jpbo-041-36 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-36.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.36 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-36 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.36 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 3 on jpbo-041-36 + Starting Ray worker on jpbo-041-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.37 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.37 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 4 on jpbo-041-37 + Starting Ray worker on jpbo-041-38 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-38.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.38 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-38 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.38 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 5 on jpbo-041-38 + Starting Ray worker on jpbo-041-39 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-39.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.39 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-39 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.39 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 6 on jpbo-041-39 + Starting Ray worker on jpbo-041-40 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-40.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.40 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-40 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.40 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 7 on jpbo-041-40 + Starting Ray worker on jpbo-041-41 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-41.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.41 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-41 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.41 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 8 on jpbo-041-41 + Starting Ray worker on jpbo-041-42 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-42.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.42 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-42 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.42 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 9 on jpbo-041-42 + Starting Ray worker on jpbo-041-43 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-43.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.43 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-43 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.43 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 10 on jpbo-041-43 + Starting Ray worker on jpbo-041-44 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-44.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.44 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-44 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.44 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 11 on jpbo-041-44 + Starting Ray worker on jpbo-041-45 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-45.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.45 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-45 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.45 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 12 on jpbo-041-45 + Starting Ray worker on jpbo-041-46 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-041-46.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.32.46 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-041-46 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.32.33:6379 --node-ip-address=10.128.32.46 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 13 on jpbo-041-46 + Waiting for cluster (56 GPUs, 14 nodes)... +Connecting to Ray at 10.128.32.33:6379 (expecting 14 nodes, 56.0 GPUs) +Ray connection established, polling for resources... +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'accelerator_type:GH200': 12.0, 'GPU': 48.0, 'CPU': 3456.0, 'memory': 9095679574016.0, 'object_store_memory': 515396075520.0, 'node:10.128.32.35': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.41': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.39': 1.0} +[Ray wait] nodes=13/14 GPUs=52.0/56.0 resources={'accelerator_type:GH200': 13.0, 'GPU': 52.0, 'object_store_memory': 558345748480.0, 'memory': 9872149315584.0, 'CPU': 3744.0, 'node:10.128.32.35': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.41': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.39': 1.0} +[Ray wait] nodes=13/14 GPUs=52.0/56.0 resources={'accelerator_type:GH200': 13.0, 'GPU': 52.0, 'object_store_memory': 558345748480.0, 'memory': 9872149315584.0, 'CPU': 3744.0, 'node:10.128.32.35': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.41': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.39': 1.0} +[Ray wait] nodes=13/14 GPUs=52.0/56.0 resources={'accelerator_type:GH200': 13.0, 'GPU': 52.0, 'CPU': 3744.0, 'memory': 9872149315584.0, 'object_store_memory': 558345748480.0, 'node:10.128.32.35': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.38': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.41': 1.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.39': 1.0} +[Ray wait] nodes=14/14 GPUs=56.0/56.0 resources={'accelerator_type:GH200': 14.0, 'GPU': 56.0, 'object_store_memory': 601295421440.0, 'memory': 10634851450880.0, 'node:10.128.32.41': 1.0, 'CPU': 4032.0, 'node:10.128.32.45': 1.0, 'node:10.128.32.34': 1.0, 'node:10.128.32.35': 1.0, 'node:10.128.32.43': 1.0, 'node:10.128.32.44': 1.0, 'node:10.128.32.40': 1.0, 'node:10.128.32.36': 1.0, 'node:10.128.32.37': 1.0, 'node:10.128.32.39': 1.0, 'node:10.128.32.33': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.32.42': 1.0, 'node:10.128.32.46': 1.0, 'node:10.128.32.38': 1.0} +✓ Ray cluster ready +=== Ray Cluster Ready === + Address: 10.128.32.33:6379 + Total GPUs: 56 +========================= +Ray cluster ready at 10.128.32.33:6379 +Total GPUs available: 56 +[RLJobRunner] Pinggy check: url=False, token=False, needs_tunnel=False (agent=terminus-2, env=daytona) +[RLJobRunner] No Pinggy tunnel needed, using local vLLM + +Running SkyRL: + Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python + Entrypoint: examples.terminal_bench.entrypoints.main_tbench + Args: 120 Hydra arguments + Working dir: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train +Using proxychains binary: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 + +Executing command with srun: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f $PROXYCHAINS_CONF_FILE /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python -m examples.terminal_bench.entrypoints.main_tbench +terminal_bench_config=terminal_bench trainer.strategy=fsdp2 trainer.algorithm.advantage_estimator=rloo_n trainer.algorithm.use_kl_loss=false trainer.algorithm.kl_loss_coef=0.0 trainer.algorithm.eps_clip_low=0.2 trainer.algorithm.eps_clip_high=0.05 trainer.algorithm.loss_reduction=sequence_mean trainer.epochs=2 trainer.max_steps=80 trainer.update_epochs_per_batch=1 trainer.train_batch_size=64 trainer.policy_mini_batch_size=64 trainer.eval_batch_size=64 trainer.micro_forward_batch_size_per_gpu=4 trainer.micro_train_batch_size_per_gpu=1 trainer.max_prompt_length=999999 trainer.eval_interval=999999 trainer.eval_before_train=false trainer.ckpt_interval=2 trainer.resume_mode=latest trainer.hf_save_interval=5 ++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0 ++trainer.hf_hub_private=false ++trainer.hf_hub_revision=main ++trainer.enable_db_registration=false trainer.project_name=OpenThoughts-Agent trainer.log_level=INFO trainer.tracker_commit_each_step=true trainer.logger=console trainer.run_name=ablation-pymethods2test-seqmean-arm0 trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports trainer.policy.optimizer_config.lr=8e-6 trainer.policy.optimizer_config.weight_decay=0.0 trainer.policy.optimizer_config.adam_betas=[0.9,0.999] trainer.policy.optimizer_config.max_grad_norm=0.9 trainer.policy.fsdp_config.cpu_offload=false trainer.policy.fsdp_config.reshard_after_forward=true trainer.policy.fsdp_config.fsdp_size=4 trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 trainer.ref.fsdp_config.cpu_offload=false trainer.ref.fsdp_config.reshard_after_forward=true trainer.ref.fsdp_config.fsdp_size=4 trainer.placement.colocate_all=false trainer.placement.policy_num_nodes=2 trainer.placement.ref_num_nodes=2 trainer.placement.policy_num_gpus_per_node=4 trainer.placement.ref_num_gpus_per_node=4 trainer.fully_async.max_staleness_steps=16 trainer.fully_async.num_parallel_generation_workers=338 generator.backend=vllm generator.timeout_multiplier=1.0 generator.model_dtype=bfloat16 generator.inference_engine_tensor_parallel_size=1 generator.num_inference_engines=48 generator.n_samples_per_prompt=8 generator.eval_n_samples_per_prompt=8 generator.gpu_memory_utilization=0.75 generator.max_num_seqs=24 generator.max_num_batched_tokens=65536 generator.enable_prefix_caching=true generator.enable_chunked_prefill=true generator.run_engines_locally=true generator.weight_sync_backend=nccl generator.async_engine=true generator.batched=false generator.enable_http_endpoint=true generator.enable_ray_prometheus_stats=false generator.vllm_stats_interval=1 generator.append_eos_token_after_stop_str_in_multi_turn=true generator.max_turns=999999 generator.sampling_params.max_generate_length=4096 generator.sampling_params.temperature=0.7 generator.sampling_params.top_p=0.95 generator.sampling_params.top_k=20 ++generator.engine_init_kwargs.max_model_len=32768 ++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2 ++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"] data.val_data=[] +terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs +terminal_bench_config.harbor.name=terminus-2 +terminal_bench_config.harbor.max_episodes=999999 +terminal_bench_config.harbor.enable_summarize=false +terminal_bench_config.harbor.store_all_messages=true +terminal_bench_config.harbor.trajectory_config.raw_content=true +terminal_bench_config.harbor.enable_episode_logging=false +terminal_bench_config.harbor.record_terminal_session=false +terminal_bench_config.harbor.enable_pane_logging=false +terminal_bench_config.harbor.strict_json_parser=true +terminal_bench_config.harbor.interleaved_thinking=true +terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true +terminal_bench_config.harbor.override_timeout_sec=900 +terminal_bench_config.harbor.override_cpus=1 +terminal_bench_config.harbor.override_memory_mb=2048 +terminal_bench_config.harbor.override_storage_mb=2048 +terminal_bench_config.harbor.auto_snapshot=true +terminal_bench_config.harbor.verifier_override_timeout_sec=120 +terminal_bench_config.harbor.max_retries=3 +terminal_bench_config.harbor.min_wait_sec=60.0 +terminal_bench_config.harbor.max_wait_sec=600.0 +terminal_bench_config.harbor.wait_multiplier=2.0 +terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"] +terminal_bench_config.harbor.n_concurrent_trials=675 +terminal_bench_config.harbor.log_level=INFO +terminal_bench_config.harbor.enable_reward_shaping=false +terminal_bench_config.harbor.enable_error_classification=true +terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"] +terminal_bench_config.harbor.default_error_treatment=zero +terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"] +terminal_bench_config.harbor.zero_exceptions=[] +terminal_bench_config.model_info.max_input_tokens=32000 +terminal_bench_config.model_info.max_output_tokens=4096 +terminal_bench_config.archiving.enabled=false +terminal_bench_config.trace_upload.enabled=true +terminal_bench_config.trace_upload.repo_org=DCAgent +terminal_bench_config.trace_upload.episodes=last +terminal_bench_config.trace_upload.dataset_type=SFT +terminal_bench_config.trace_upload.cleanup=true +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_630129.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-07 16:02:29.538 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:753 - Exporting wandb api key to ray runtime env +2026-06-07 16:02:29.538 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:772 - Exporting RAY_ADDRESS to ray runtime env +2026-06-07 16:02:29.538 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:797 - Exporting `NCCL_SOCKET_IFNAME` to ray runtime env: ib0 +2026-06-07 16:02:29.538 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:797 - Exporting `NCCL_SOCKET_FAMILY` to ray runtime env: AF_INET +2026-06-07 16:02:29.538 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:797 - Exporting `NCCL_DEBUG` to ray runtime env: WARN +2026-06-07 16:02:29,539 INFO worker.py:1680 -- Using address 10.128.32.33:6379 set in the environment variable RAY_ADDRESS +2026-06-07 16:02:29,574 INFO worker.py:1821 -- Connecting to existing Ray cluster at address: 10.128.32.33:6379... +2026-06-07 16:02:29,585 INFO worker.py:2007 -- Connected to Ray cluster. +/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0 + warnings.warn( +(raylet, ip=10.128.32.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-07 16:02:32.338 | INFO | skyrl_train.utils.ppo_utils:sync_registries:546 - Synced registries to ray actor +(skyrl_entrypoint pid=391889) 2026-06-07 16:02:41.646 | INFO  | skyrl_train.entrypoints.main_base:_configure_log_level:212 - SkyRL log level set to: INFO +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 17x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) +(skyrl_entrypoint pid=391889) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(skyrl_entrypoint pid=391889) 2026-06-07 16:02:42.007 | INFO  | examples.terminal_bench.dataset:_load_data_files:40 - Loading data from: /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +[2026-06-07 16:02:59,789 E 391407 391863] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RegistryActor pid=2613231, ip=10.128.32.41) [2026-06-07 16:03:00,820 E 2613231 2613271] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(skyrl_entrypoint pid=391889) 2026-06-07 16:03:39.460 | INFO  | examples.terminal_bench.dataset:_load_data_files:50 - Found 5000 valid task directories out of 5000 total directories +(skyrl_entrypoint pid=391889) 2026-06-07 16:03:39.461 | INFO  | examples.terminal_bench.dataset:__init__:27 - TerminalBenchTaskDataset initialized with 5000 task paths +(skyrl_entrypoint pid=391889) 2026-06-07 16:03:39.473 | INFO  | skyrl_train.entrypoints.main_base:_setup_trainer:405 - data: +(skyrl_entrypoint pid=391889) train_data: +(skyrl_entrypoint pid=391889) - /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=391889) val_data: [] +(skyrl_entrypoint pid=391889) trainer: +(skyrl_entrypoint pid=391889) placement: +(skyrl_entrypoint pid=391889) colocate_all: false +(skyrl_entrypoint pid=391889) colocate_policy_ref: true +(skyrl_entrypoint pid=391889) policy_num_nodes: 2 +(skyrl_entrypoint pid=391889) policy_num_gpus_per_node: 4 +(skyrl_entrypoint pid=391889) critic_num_nodes: 1 +(skyrl_entrypoint pid=391889) critic_num_gpus_per_node: 4 +(skyrl_entrypoint pid=391889) ref_num_nodes: 2 +(skyrl_entrypoint pid=391889) ref_num_gpus_per_node: 4 +(skyrl_entrypoint pid=391889) policy_strict_spread_pg: false +(skyrl_entrypoint pid=391889) policy_per_gpu_bundles: false +(skyrl_entrypoint pid=391889) policy_force_cvd_mask: false +(skyrl_entrypoint pid=391889) sequence_parallel_backend: ulysses +(skyrl_entrypoint pid=391889) strategy: fsdp2 +(skyrl_entrypoint pid=391889) policy: +(skyrl_entrypoint pid=391889) model: +(skyrl_entrypoint pid=391889) path: /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=391889) lora: +(skyrl_entrypoint pid=391889) rank: 0 +(skyrl_entrypoint pid=391889) alpha: 16 +(skyrl_entrypoint pid=391889) dropout: 0 +(skyrl_entrypoint pid=391889) lora_sync_path: /tmp/skyrl_lora_sync +(skyrl_entrypoint pid=391889) target_modules: all-linear +(skyrl_entrypoint pid=391889) exclude_modules: null +(skyrl_entrypoint pid=391889) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=391889) optimizer_config: +(skyrl_entrypoint pid=391889) optimizer: AdamW +(skyrl_entrypoint pid=391889) lr: 8.0e-06 +(skyrl_entrypoint pid=391889) adam_betas: +(skyrl_entrypoint pid=391889) - 0.9 +(skyrl_entrypoint pid=391889) - 0.999 +(skyrl_entrypoint pid=391889) weight_decay: 0.0 +(skyrl_entrypoint pid=391889) max_grad_norm: 0.9 +(skyrl_entrypoint pid=391889) offload_after_step: true +(skyrl_entrypoint pid=391889) num_warmup_steps: 0 +(skyrl_entrypoint pid=391889) scheduler: constant_with_warmup +(skyrl_entrypoint pid=391889) optimizer_kwargs: {} +(skyrl_entrypoint pid=391889) fsdp_config: +(skyrl_entrypoint pid=391889) cpu_offload: false +(skyrl_entrypoint pid=391889) reshard_after_forward: true +(skyrl_entrypoint pid=391889) fsdp_size: 4 +(skyrl_entrypoint pid=391889) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=391889) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=391889) moe_router_replay: false +(skyrl_entrypoint pid=391889) moe_grouped_gemm: false +(skyrl_entrypoint pid=391889) ep_comm_backend: torch +(skyrl_entrypoint pid=391889) deepep_num_sms: 20 +(skyrl_entrypoint pid=391889) deepep_token_chunk_size: null +(skyrl_entrypoint pid=391889) sequence_parallel_size: 1 +(skyrl_entrypoint pid=391889) use_torch_compile: false +(skyrl_entrypoint pid=391889) record_memory: false +(skyrl_entrypoint pid=391889) megatron_config: +(skyrl_entrypoint pid=391889) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) context_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_tensor_parallel_size: null +(skyrl_entrypoint pid=391889) ddp_config: +(skyrl_entrypoint pid=391889) grad_reduce_in_fp32: true +(skyrl_entrypoint pid=391889) overlap_grad_reduce: false +(skyrl_entrypoint pid=391889) overlap_param_gather: false +(skyrl_entrypoint pid=391889) average_in_collective: true +(skyrl_entrypoint pid=391889) model_config_kwargs: {} +(skyrl_entrypoint pid=391889) torch_profiler_config: +(skyrl_entrypoint pid=391889) enable: false +(skyrl_entrypoint pid=391889) ranks: [] +(skyrl_entrypoint pid=391889) save_path: null +(skyrl_entrypoint pid=391889) optimizer_config_kwargs: +(skyrl_entrypoint pid=391889) overlap_cpu_optimizer_d2h_h2d: false +(skyrl_entrypoint pid=391889) use_precision_aware_optimizer: false +(skyrl_entrypoint pid=391889) optimizer_cpu_offload: false +(skyrl_entrypoint pid=391889) optimizer_offload_fraction: 0.0 +(skyrl_entrypoint pid=391889) transformer_config_kwargs: +(skyrl_entrypoint pid=391889) recompute_granularity: full +(skyrl_entrypoint pid=391889) recompute_modules: +(skyrl_entrypoint pid=391889) - core_attn +(skyrl_entrypoint pid=391889) recompute_method: uniform +(skyrl_entrypoint pid=391889) recompute_num_layers: 1 +(skyrl_entrypoint pid=391889) empty_cuda_cache: true +(skyrl_entrypoint pid=391889) ref: +(skyrl_entrypoint pid=391889) model: +(skyrl_entrypoint pid=391889) path: ${trainer.policy.model.path} +(skyrl_entrypoint pid=391889) sequence_parallel_size: 1 +(skyrl_entrypoint pid=391889) deepspeed_config: ${deepspeed_config.eval} +(skyrl_entrypoint pid=391889) fsdp_config: +(skyrl_entrypoint pid=391889) cpu_offload: false +(skyrl_entrypoint pid=391889) reshard_after_forward: true +(skyrl_entrypoint pid=391889) fsdp_size: 4 +(skyrl_entrypoint pid=391889) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=391889) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=391889) moe_router_replay: false +(skyrl_entrypoint pid=391889) moe_grouped_gemm: false +(skyrl_entrypoint pid=391889) ep_comm_backend: torch +(skyrl_entrypoint pid=391889) deepep_num_sms: 20 +(skyrl_entrypoint pid=391889) deepep_token_chunk_size: null +(skyrl_entrypoint pid=391889) megatron_config: +(skyrl_entrypoint pid=391889) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) context_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=391889) model_config_kwargs: {} +(skyrl_entrypoint pid=391889) transformer_config_kwargs: {} +(skyrl_entrypoint pid=391889) critic: +(skyrl_entrypoint pid=391889) model: +(skyrl_entrypoint pid=391889) path: null +(skyrl_entrypoint pid=391889) lora: +(skyrl_entrypoint pid=391889) rank: 0 +(skyrl_entrypoint pid=391889) alpha: 16 +(skyrl_entrypoint pid=391889) dropout: 0 +(skyrl_entrypoint pid=391889) target_modules: all-linear +(skyrl_entrypoint pid=391889) exclude_modules: null +(skyrl_entrypoint pid=391889) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=391889) optimizer_config: +(skyrl_entrypoint pid=391889) optimizer: AdamW +(skyrl_entrypoint pid=391889) lr: 5.0e-06 +(skyrl_entrypoint pid=391889) adam_betas: +(skyrl_entrypoint pid=391889) - 0.9 +(skyrl_entrypoint pid=391889) - 0.999 +(skyrl_entrypoint pid=391889) weight_decay: 0.01 +(skyrl_entrypoint pid=391889) max_grad_norm: 1.0 +(skyrl_entrypoint pid=391889) offload_after_step: true +(skyrl_entrypoint pid=391889) num_warmup_steps: 0 +(skyrl_entrypoint pid=391889) scheduler: constant_with_warmup +(skyrl_entrypoint pid=391889) optimizer_kwargs: {} +(skyrl_entrypoint pid=391889) fsdp_config: +(skyrl_entrypoint pid=391889) cpu_offload: false +(skyrl_entrypoint pid=391889) reshard_after_forward: true +(skyrl_entrypoint pid=391889) fsdp_size: -1 +(skyrl_entrypoint pid=391889) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=391889) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=391889) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=391889) moe_router_replay: false +(skyrl_entrypoint pid=391889) moe_grouped_gemm: false +(skyrl_entrypoint pid=391889) ep_comm_backend: torch +(skyrl_entrypoint pid=391889) deepep_num_sms: 20 +(skyrl_entrypoint pid=391889) deepep_token_chunk_size: null +(skyrl_entrypoint pid=391889) sequence_parallel_size: 1 +(skyrl_entrypoint pid=391889) algorithm: +(skyrl_entrypoint pid=391889) advantage_estimator: rloo_n +(skyrl_entrypoint pid=391889) kl_ctrl: +(skyrl_entrypoint pid=391889) type: fixed +(skyrl_entrypoint pid=391889) kl_target: 0.1 +(skyrl_entrypoint pid=391889) horizon: 10000 +(skyrl_entrypoint pid=391889) kl_estimator_type: k3 +(skyrl_entrypoint pid=391889) use_kl_estimator_k3: false +(skyrl_entrypoint pid=391889) use_abs_kl: false +(skyrl_entrypoint pid=391889) use_kl_in_reward: false +(skyrl_entrypoint pid=391889) use_kl_loss: false +(skyrl_entrypoint pid=391889) kl_loss_coef: 0.0 +(skyrl_entrypoint pid=391889) use_entropy_loss: false +(skyrl_entrypoint pid=391889) entropy_loss_coef: 0.01 +(skyrl_entrypoint pid=391889) advantage_batch_normalize: false +(skyrl_entrypoint pid=391889) value_head_prefix: value_head +(skyrl_entrypoint pid=391889) policy_loss_type: regular +(skyrl_entrypoint pid=391889) loss_reduction: sequence_mean +(skyrl_entrypoint pid=391889) global_loss_denom: null +(skyrl_entrypoint pid=391889) grpo_norm_by_std: true +(skyrl_entrypoint pid=391889) rloo_n_min_group_size: 4 +(skyrl_entrypoint pid=391889) rloo_n_filter_zero_reward_groups: true +(skyrl_entrypoint pid=391889) lambd: 1.0 +(skyrl_entrypoint pid=391889) gamma: 1.0 +(skyrl_entrypoint pid=391889) eps_clip_low: 0.2 +(skyrl_entrypoint pid=391889) eps_clip_high: 0.05 +(skyrl_entrypoint pid=391889) clip_ratio_c: 3.0 +(skyrl_entrypoint pid=391889) tis_imp_ratio_cap: -1.0 +(skyrl_entrypoint pid=391889) use_tis: false +(skyrl_entrypoint pid=391889) sapo: +(skyrl_entrypoint pid=391889) tau_pos: 1.0 +(skyrl_entrypoint pid=391889) tau_neg: 1.05 +(skyrl_entrypoint pid=391889) value_clip: 0.2 +(skyrl_entrypoint pid=391889) dynamic_sampling: +(skyrl_entrypoint pid=391889) type: null +(skyrl_entrypoint pid=391889) max_sample_batches: 30 +(skyrl_entrypoint pid=391889) min_replace_ratio: 0.3 +(skyrl_entrypoint pid=391889) clip_cov: +(skyrl_entrypoint pid=391889) clip_ratio: 0.0002 +(skyrl_entrypoint pid=391889) clip_cov_lb: 1.0 +(skyrl_entrypoint pid=391889) clip_cov_ub: 5.0 +(skyrl_entrypoint pid=391889) kl_cov: +(skyrl_entrypoint pid=391889) kl_cov_frac: 0.2 +(skyrl_entrypoint pid=391889) ppo_kl_coef: 1.0 +(skyrl_entrypoint pid=391889) cispo: +(skyrl_entrypoint pid=391889) cispo_eps_clip_low: 0 +(skyrl_entrypoint pid=391889) cispo_eps_clip_high: 5 +(skyrl_entrypoint pid=391889) z_clip: +(skyrl_entrypoint pid=391889) enabled: false +(skyrl_entrypoint pid=391889) alpha: 0.97 +(skyrl_entrypoint pid=391889) z_thresh: 2.5 +(skyrl_entrypoint pid=391889) warmup_steps: 25 +(skyrl_entrypoint pid=391889) mode: zscore +(skyrl_entrypoint pid=391889) clip_option: adaptive_scaling +(skyrl_entrypoint pid=391889) clip_factor: 1.0 +(skyrl_entrypoint pid=391889) skip_update_on_spike: false +(skyrl_entrypoint pid=391889) stale_clip: +(skyrl_entrypoint pid=391889) enabled: false +(skyrl_entrypoint pid=391889) alpha: 0.3 +(skyrl_entrypoint pid=391889) entropy_threshold: 0.15 +(skyrl_entrypoint pid=391889) entropy_window: 10 +(skyrl_entrypoint pid=391889) min_lr_scale: 0.1 +(skyrl_entrypoint pid=391889) max_seq_len: 1004095 +(skyrl_entrypoint pid=391889) fully_async: +(skyrl_entrypoint pid=391889) max_staleness_steps: 16 +(skyrl_entrypoint pid=391889) num_parallel_generation_workers: 338 +(skyrl_entrypoint pid=391889) gradient_checkpointing: true +(skyrl_entrypoint pid=391889) gradient_checkpointing_use_reentrant: false +(skyrl_entrypoint pid=391889) seed: 42 +(skyrl_entrypoint pid=391889) resume_mode: latest +(skyrl_entrypoint pid=391889) resume_path: null +(skyrl_entrypoint pid=391889) ckpt_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints +(skyrl_entrypoint pid=391889) max_ckpts_to_keep: -1 +(skyrl_entrypoint pid=391889) ckpt_interval: 2 +(skyrl_entrypoint pid=391889) hf_save_interval: 5 +(skyrl_entrypoint pid=391889) hf_upload_mode: latest +(skyrl_entrypoint pid=391889) export_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports +(skyrl_entrypoint pid=391889) bf16: true +(skyrl_entrypoint pid=391889) epochs: 2 +(skyrl_entrypoint pid=391889) max_steps: 80 +(skyrl_entrypoint pid=391889) update_epochs_per_batch: 1 +(skyrl_entrypoint pid=391889) train_batch_size: 64 +(skyrl_entrypoint pid=391889) policy_mini_batch_size: 64 +(skyrl_entrypoint pid=391889) critic_mini_batch_size: 256 +(skyrl_entrypoint pid=391889) micro_train_batch_size_per_gpu: 1 +(skyrl_entrypoint pid=391889) micro_forward_batch_size_per_gpu: 4 +(skyrl_entrypoint pid=391889) update_ref_every_epoch: false +(skyrl_entrypoint pid=391889) use_sample_packing: true +(skyrl_entrypoint pid=391889) eval_batch_size: 64 +(skyrl_entrypoint pid=391889) eval_before_train: false +(skyrl_entrypoint pid=391889) eval_interval: 999999 +(skyrl_entrypoint pid=391889) max_prompt_length: 999999 +(skyrl_entrypoint pid=391889) flash_attn: true +(skyrl_entrypoint pid=391889) disable_fast_tokenizer: false +(skyrl_entrypoint pid=391889) target_modules: null +(skyrl_entrypoint pid=391889) exclude_modules: null +(skyrl_entrypoint pid=391889) project_name: OpenThoughts-Agent +(skyrl_entrypoint pid=391889) run_name: ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=391889) logger: console +(skyrl_entrypoint pid=391889) tracker_commit_each_step: true +(skyrl_entrypoint pid=391889) dump_data_batch: false +(skyrl_entrypoint pid=391889) dump_eval_results: true +(skyrl_entrypoint pid=391889) log_level: INFO +(skyrl_entrypoint pid=391889) rope_scaling: null +(skyrl_entrypoint pid=391889) rope_theta: null +(skyrl_entrypoint pid=391889) step_wise_training: false +(skyrl_entrypoint pid=391889) hf_hub_repo_id: laion/ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=391889) hf_hub_private: false +(skyrl_entrypoint pid=391889) hf_hub_revision: main +(skyrl_entrypoint pid=391889) enable_db_registration: false +(skyrl_entrypoint pid=391889) generator: +(skyrl_entrypoint pid=391889) model_name: ${trainer.policy.model.path} +(skyrl_entrypoint pid=391889) model_dtype: bfloat16 +(skyrl_entrypoint pid=391889) timeout_multiplier: 1.0 +(skyrl_entrypoint pid=391889) run_engines_locally: true +(skyrl_entrypoint pid=391889) num_inference_engines: 48 +(skyrl_entrypoint pid=391889) backend: vllm +(skyrl_entrypoint pid=391889) weight_sync_backend: nccl +(skyrl_entrypoint pid=391889) fuse_weights: false +(skyrl_entrypoint pid=391889) weight_transfer_threshold_cuda_ipc_GB: 1.0 +(skyrl_entrypoint pid=391889) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=391889) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=391889) inference_engine_expert_parallel_size: 1 +(skyrl_entrypoint pid=391889) inference_engine_data_parallel_size: 1 +(skyrl_entrypoint pid=391889) n_samples_per_prompt: 8 +(skyrl_entrypoint pid=391889) async_engine: true +(skyrl_entrypoint pid=391889) batched: false +(skyrl_entrypoint pid=391889) max_input_length: ${trainer.max_prompt_length} +(skyrl_entrypoint pid=391889) vllm_v1_disable_multiproc: true +(skyrl_entrypoint pid=391889) enable_prefix_caching: true +(skyrl_entrypoint pid=391889) enable_chunked_prefill: true +(skyrl_entrypoint pid=391889) max_num_batched_tokens: 65536 +(skyrl_entrypoint pid=391889) enforce_eager: true +(skyrl_entrypoint pid=391889) fully_sharded_loras: false +(skyrl_entrypoint pid=391889) enable_ray_prometheus_stats: false +(skyrl_entrypoint pid=391889) vllm_stats_interval: 1 +(skyrl_entrypoint pid=391889) gpu_memory_utilization: 0.75 +(skyrl_entrypoint pid=391889) max_num_seqs: 24 +(skyrl_entrypoint pid=391889) remote_inference_engine_urls: +(skyrl_entrypoint pid=391889) - 127.0.0.1:8001 +(skyrl_entrypoint pid=391889) enable_http_endpoint: true +(skyrl_entrypoint pid=391889) http_endpoint_host: 127.0.0.1 +(skyrl_entrypoint pid=391889) http_endpoint_port: 8000 +(skyrl_entrypoint pid=391889) max_turns: 999999 +(skyrl_entrypoint pid=391889) chat_template: +(skyrl_entrypoint pid=391889) source: name +(skyrl_entrypoint pid=391889) name_or_path: null +(skyrl_entrypoint pid=391889) chat_template_kwargs: {} +(skyrl_entrypoint pid=391889) engine_init_kwargs: +(skyrl_entrypoint pid=391889) max_model_len: 32768 +(skyrl_entrypoint pid=391889) custom_chat_template_chat_completion_path: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=391889) served_model_name: 0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=391889) override_existing_update_group: disable +(skyrl_entrypoint pid=391889) sampling_params: +(skyrl_entrypoint pid=391889) max_generate_length: 4096 +(skyrl_entrypoint pid=391889) repetition_penalty: 1.0 +(skyrl_entrypoint pid=391889) temperature: 0.7 +(skyrl_entrypoint pid=391889) top_p: 0.95 +(skyrl_entrypoint pid=391889) min_p: 0.0 +(skyrl_entrypoint pid=391889) top_k: 20 +(skyrl_entrypoint pid=391889) logprobs: null +(skyrl_entrypoint pid=391889) stop: null +(skyrl_entrypoint pid=391889) use_conversation_multi_turn: true +(skyrl_entrypoint pid=391889) append_eos_token_after_stop_str_in_multi_turn: true +(skyrl_entrypoint pid=391889) eval_sampling_params: +(skyrl_entrypoint pid=391889) max_generate_length: ${generator.sampling_params.max_generate_length} +(skyrl_entrypoint pid=391889) repetition_penalty: 1.0 +(skyrl_entrypoint pid=391889) temperature: 0.0 +(skyrl_entrypoint pid=391889) top_p: 1.0 +(skyrl_entrypoint pid=391889) min_p: 0.0 +(skyrl_entrypoint pid=391889) top_k: -1 +(skyrl_entrypoint pid=391889) logprobs: null +(skyrl_entrypoint pid=391889) stop: null +(skyrl_entrypoint pid=391889) eval_n_samples_per_prompt: 8 +(skyrl_entrypoint pid=391889) zero_reward_on_non_stop: false +(skyrl_entrypoint pid=391889) apply_overlong_filtering: false +(skyrl_entrypoint pid=391889) rope_scaling: ${trainer.rope_scaling} +(skyrl_entrypoint pid=391889) rope_theta: ${trainer.rope_theta} +(skyrl_entrypoint pid=391889) teacher: +(skyrl_entrypoint pid=391889) model_path: null +(skyrl_entrypoint pid=391889) top_k_logprobs: 256 +(skyrl_entrypoint pid=391889) num_inference_engines: 1 +(skyrl_entrypoint pid=391889) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=391889) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=391889) gpu_memory_utilization: 0.9 +(skyrl_entrypoint pid=391889) enforce_eager: false +(skyrl_entrypoint pid=391889) backend: vllm +(skyrl_entrypoint pid=391889) engine_init_kwargs: {} +(skyrl_entrypoint pid=391889) environment: +(skyrl_entrypoint pid=391889) env_class: gsm8k +(skyrl_entrypoint pid=391889) skyrl_gym: +(skyrl_entrypoint pid=391889) max_env_workers: 32 +(skyrl_entrypoint pid=391889) text2sql: +(skyrl_entrypoint pid=391889) db_path: /home/ray/default/sql_data +(skyrl_entrypoint pid=391889) llm_as_a_judge: +(skyrl_entrypoint pid=391889) model: gpt-4o-mini +(skyrl_entrypoint pid=391889) base_url: null +(skyrl_entrypoint pid=391889) search: +(skyrl_entrypoint pid=391889) log_requests: false +(skyrl_entrypoint pid=391889) search_url: http://127.0.0.1:8000/retrieve +(skyrl_entrypoint pid=391889) topk: 3 +(skyrl_entrypoint pid=391889) timeout: 30 +(skyrl_entrypoint pid=391889) rollout: +(skyrl_entrypoint pid=391889) fanout: +(skyrl_entrypoint pid=391889) enabled: true +(skyrl_entrypoint pid=391889) num_coordinators: 4 +(skyrl_entrypoint pid=391889) cpus_per_coordinator: 8 +(skyrl_entrypoint pid=391889) deepspeed_config: +(skyrl_entrypoint pid=391889) train: +(skyrl_entrypoint pid=391889) zero_optimization: +(skyrl_entrypoint pid=391889) stage: 3 +(skyrl_entrypoint pid=391889) offload_param: +(skyrl_entrypoint pid=391889) device: none +(skyrl_entrypoint pid=391889) offload_optimizer: +(skyrl_entrypoint pid=391889) device: none +(skyrl_entrypoint pid=391889) pin_memory: true +(skyrl_entrypoint pid=391889) sub_group_size: auto +(skyrl_entrypoint pid=391889) reduce_bucket_size: auto +(skyrl_entrypoint pid=391889) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=391889) stage3_prefetch_bucket_size: auto +(skyrl_entrypoint pid=391889) stage3_max_live_parameters: auto +(skyrl_entrypoint pid=391889) stage3_max_reuse_distance: auto +(skyrl_entrypoint pid=391889) round_robin_gradients: true +(skyrl_entrypoint pid=391889) zero_hpz_partition_size: 1 +(skyrl_entrypoint pid=391889) zero_quantized_weights: false +(skyrl_entrypoint pid=391889) zero_quantized_gradients: false +(skyrl_entrypoint pid=391889) torch_autocast: +(skyrl_entrypoint pid=391889) enabled: true +(skyrl_entrypoint pid=391889) dtype: bfloat16 +(skyrl_entrypoint pid=391889) disable_trace_cache: false +(skyrl_entrypoint pid=391889) data_types: +(skyrl_entrypoint pid=391889) grad_accum_dtype: fp32 +(skyrl_entrypoint pid=391889) gradient_clipping: 1.0 +(skyrl_entrypoint pid=391889) wall_clock_breakdown: false +(skyrl_entrypoint pid=391889) prescale_gradient: false +(skyrl_entrypoint pid=391889) eval: +(skyrl_entrypoint pid=391889) zero_optimization: +(skyrl_entrypoint pid=391889) stage: 3 +(skyrl_entrypoint pid=391889) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=391889) offload_param: +(skyrl_entrypoint pid=391889) device: cpu +(skyrl_entrypoint pid=391889) pin_memory: true +(skyrl_entrypoint pid=391889) torch_autocast: +(skyrl_entrypoint pid=391889) enabled: true +(skyrl_entrypoint pid=391889) dtype: bfloat16 +(skyrl_entrypoint pid=391889) gradient_clipping: 1.0 +(skyrl_entrypoint pid=391889) prescale_gradient: false +(skyrl_entrypoint pid=391889) wall_clock_breakdown: false +(skyrl_entrypoint pid=391889) terminal_bench_config: +(skyrl_entrypoint pid=391889) trials_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs +(skyrl_entrypoint pid=391889) harbor: +(skyrl_entrypoint pid=391889) name: terminus-2 +(skyrl_entrypoint pid=391889) max_episodes: 999999 +(skyrl_entrypoint pid=391889) enable_summarize: false +(skyrl_entrypoint pid=391889) store_all_messages: true +(skyrl_entrypoint pid=391889) trajectory_config: +(skyrl_entrypoint pid=391889) raw_content: true +(skyrl_entrypoint pid=391889) enable_episode_logging: false +(skyrl_entrypoint pid=391889) record_terminal_session: false +(skyrl_entrypoint pid=391889) enable_pane_logging: false +(skyrl_entrypoint pid=391889) strict_json_parser: true +(skyrl_entrypoint pid=391889) interleaved_thinking: true +(skyrl_entrypoint pid=391889) extra_body: +(skyrl_entrypoint pid=391889) chat_template_kwargs: +(skyrl_entrypoint pid=391889) enable_thinking: true +(skyrl_entrypoint pid=391889) override_timeout_sec: 900 +(skyrl_entrypoint pid=391889) override_cpus: 1 +(skyrl_entrypoint pid=391889) override_memory_mb: 2048 +(skyrl_entrypoint pid=391889) override_storage_mb: 2048 +(skyrl_entrypoint pid=391889) auto_snapshot: true +(skyrl_entrypoint pid=391889) verifier_override_timeout_sec: 120 +(skyrl_entrypoint pid=391889) max_retries: 3 +(skyrl_entrypoint pid=391889) min_wait_sec: 60.0 +(skyrl_entrypoint pid=391889) max_wait_sec: 600.0 +(skyrl_entrypoint pid=391889) wait_multiplier: 2.0 +(skyrl_entrypoint pid=391889) exclude_exceptions: +(skyrl_entrypoint pid=391889) - VerifierTimeoutError +(skyrl_entrypoint pid=391889) - VerifierRuntimeError +(skyrl_entrypoint pid=391889) - RewardFileNotFoundError +(skyrl_entrypoint pid=391889) - RewardFileEmptyError +(skyrl_entrypoint pid=391889) - VerifierOutputParseError +(skyrl_entrypoint pid=391889) n_concurrent_trials: 675 +(skyrl_entrypoint pid=391889) log_level: INFO +(skyrl_entrypoint pid=391889) enable_reward_shaping: false +(skyrl_entrypoint pid=391889) enable_error_classification: true +(skyrl_entrypoint pid=391889) mask_exceptions: +(skyrl_entrypoint pid=391889) - DaytonaError +(skyrl_entrypoint pid=391889) - EnvironmentStartTimeoutError +(skyrl_entrypoint pid=391889) - NetworkError +(skyrl_entrypoint pid=391889) - ConnectionError +(skyrl_entrypoint pid=391889) - RewardFileNotFoundError +(skyrl_entrypoint pid=391889) - RewardFileEmptyError +(skyrl_entrypoint pid=391889) - AgentEnvironmentTimeoutError +(skyrl_entrypoint pid=391889) - ContextLengthExceededError +(skyrl_entrypoint pid=391889) default_error_treatment: zero +(skyrl_entrypoint pid=391889) passthrough_exceptions: +(skyrl_entrypoint pid=391889) - AgentTimeoutError +(skyrl_entrypoint pid=391889) zero_exceptions: [] +(skyrl_entrypoint pid=391889) model_info: +(skyrl_entrypoint pid=391889) max_input_tokens: 32000 +(skyrl_entrypoint pid=391889) max_output_tokens: 4096 +(skyrl_entrypoint pid=391889) archiving: +(skyrl_entrypoint pid=391889) enabled: false +(skyrl_entrypoint pid=391889) trace_upload: +(skyrl_entrypoint pid=391889) enabled: true +(skyrl_entrypoint pid=391889) repo_org: DCAgent +(skyrl_entrypoint pid=391889) episodes: last +(skyrl_entrypoint pid=391889) dataset_type: SFT +(skyrl_entrypoint pid=391889) cleanup: true +(skyrl_entrypoint pid=391889)  +(skyrl_entrypoint pid=391889) [2026-06-07 16:03:03,420 E 391889 391932] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 2x across cluster] +(skyrl_entrypoint pid=391889) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(skyrl_entrypoint pid=391889) No module named 'vllm._version' +(skyrl_entrypoint pid=391889) from .version import __version__, __version_tuple__ # isort:skip +(skyrl_entrypoint pid=391889) W0607 16:06:28.744000 391889 envs/rl/lib/python3.12/site-packages/torch/utils/cpp_extension.py:117] No CUDA runtime is found, using CUDA_HOME='/e/software/default/stages/2026/software/CUDA/13' +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(get_all_env_variables pid=392160) [2026-06-07 16:07:18,837 E 392160 392200] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 29x across cluster] +(pid=3113603, ip=10.128.32.42) [2026-06-07 16:07:21,100 E 3113603 3113706] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 2x across cluster] +(pid=3113603, ip=10.128.32.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=3113603, ip=10.128.32.42) No module named 'vllm._version' +(pid=3113603, ip=10.128.32.42) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 10x across cluster] +(get_addr_port pid=1022558, ip=10.128.32.37) [2026-06-07 16:08:25,572 E 1022558 1022633] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.35) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1022559, ip=10.128.32.37) [2026-06-07 16:08:25,619 E 1022559 1022661] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(get_addr_port pid=899452, ip=10.128.32.35) [2026-06-07 16:13:12,407 E 899452 899534] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 23x across cluster] +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:18.252 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:18.253 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:18.253 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(pid=1022866, ip=10.128.32.37) [2026-06-07 16:13:12,454 E 1022866 1022968] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:18.863 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:19.203 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:19.203 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:19.203 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:19.203 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) 2026-06-07 16:16:19.249 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.96s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.32.35) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 3x across cluster] +(pid=2697284, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=2697284, ip=10.128.32.43) No module named 'vllm._version' +(pid=2697284, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 81x across cluster] +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) _C._set_float32_matmul_precision(precision) +(pid=3114114, ip=10.128.32.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 6x across cluster] +(pid=3114114, ip=10.128.32.42) No module named 'vllm._version' [repeated 6x across cluster] +(pid=3114114, ip=10.128.32.42) from .version import __version__, __version_tuple__ # isort:skip [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) [W607 16:18:19.012223938 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-42-interconnect-1.jupiter.internal]:48887 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) [W607 16:18:19.013894942 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) [rank0]:[W607 16:18:19.016313609 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:37.080 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:37.080 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(pid=3114114, ip=10.128.32.42) [2026-06-07 16:18:30,329 E 3114114 3114278] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:38.033 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 144-215 (NUMA node 2) for GPU 2 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:38.037 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 2 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:38.037 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:38.037 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:38.038 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) 2026-06-07 16:18:38.082 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 1.57s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:37.079 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:37.080 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:37.080 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:38.037 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:38.040 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:38.040 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:38.040 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:38.040 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) 2026-06-07 16:18:38.082 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.51s (attempt 1/5) to avoid port collisions [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) No module named 'vllm._version' [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) from .version import __version__, __version_tuple__ # isort:skip [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) [W607 16:18:52.506534225 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-42.jupiter.internal]:44549 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) [W607 16:18:52.507043782 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) [rank0]:[W607 16:18:52.509417811 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:40.952 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:40.952 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(raylet, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 23x across cluster] +(pid=3011432, ip=10.128.32.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=3011432, ip=10.128.32.44) No module named 'vllm._version' [repeated 3x across cluster] +(pid=3011432, ip=10.128.32.44) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:42.189 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:42.404 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:42.404 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:42.404 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:42.404 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) 2026-06-07 16:28:42.452 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.34s (attempt 1/5) to avoid port collisions +(get_addr_port pid=2705576, ip=10.128.32.38) [2026-06-07 16:28:59,577 E 2705576 2705650] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:40.941 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:40.952 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:40.952 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:42.192 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:42.404 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:42.404 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:42.404 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:42.404 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) 2026-06-07 16:28:42.452 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.44s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(pid=3011432, ip=10.128.32.44) [2026-06-07 16:28:59,741 E 3011432 3011535] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(raylet, ip=10.128.32.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(get_addr_port pid=2614264, ip=10.128.32.41) [2026-06-07 16:31:12,398 E 2614264 2614340] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 28x across cluster] +(pid=2705803, ip=10.128.32.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 4x across cluster] +(pid=2705803, ip=10.128.32.38) No module named 'vllm._version' [repeated 4x across cluster] +(pid=2705803, ip=10.128.32.38) from .version import __version__, __version_tuple__ # isort:skip [repeated 4x across cluster] +(pid=2705803, ip=10.128.32.38) [2026-06-07 16:31:12,700 E 2705803 2705968] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(raylet, ip=10.128.32.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 3x across cluster] +(pid=3011671, ip=10.128.32.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=3011671, ip=10.128.32.44) No module named 'vllm._version' +(pid=3011671, ip=10.128.32.44) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.32.39) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 53x across cluster] +(pid=2631820, ip=10.128.32.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=2631820, ip=10.128.32.36) No module named 'vllm._version' [repeated 3x across cluster] +(pid=2631820, ip=10.128.32.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(get_addr_port pid=2831549, ip=10.128.32.45) [2026-06-07 16:32:34,010 E 2831549 2831630] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.39) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 28x across cluster] +(pid=2831686, ip=10.128.32.45) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 4x across cluster] +(pid=2831686, ip=10.128.32.45) No module named 'vllm._version' [repeated 4x across cluster] +(pid=2831686, ip=10.128.32.45) from .version import __version__, __version_tuple__ # isort:skip [repeated 4x across cluster] +(pid=2831684, ip=10.128.32.45) [2026-06-07 16:33:02,327 E 2831684 2831794] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:47.395 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:47.396 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:47.396 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(pid=2994914, ip=10.128.32.39) [2026-06-07 16:33:02,705 E 2994914 2995017] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:48.608 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:48.637 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:48.637 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:48.637 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:48.637 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:37:48.667 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.38s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) from .version import __version__, __version_tuple__ # isort:skip +(skyrl_entrypoint pid=391889) [2026-06-07 16:38:35] INFO inference_engine_client_http_endpoint.py:350: Starting server on 0.0.0.0:8000 +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:47.395 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:47.396 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:47.396 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:48.609 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:48.636 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:48.637 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:48.637 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:48.637 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) 2026-06-07 16:37:48.668 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.17s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(skyrl_entrypoint pid=391889) [2026-06-07 16:38:35] INFO inference_engine_client_http_endpoint.py:242: Starting inference HTTP endpoint... +(skyrl_entrypoint pid=391889) [2026-06-07 16:38:36] INFO inference_engine_client_http_endpoint.py:229: Server ready after 2 attempts (2 seconds) +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.246 | INFO  | skyrl_train.inference_engines.inference_engine_client:_spin_up_http_endpoint:969 - InferenceEngineClient HTTP endpoint started on 127.0.0.1:8000 +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.246 | INFO  | skyrl_train.inference_engines.inference_engine_client:__init__:61 - InferenceEngineClient initialized with 48 engines. +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.249 | INFO  | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:242 - Harbor logging level set to INFO +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.250 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:142 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 675. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.251 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:158 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.252 | INFO  | skyrl_train.utils.trainer_utils:build_dataloader:656 - Total steps: 156 +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.252 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:357 - Length of train_dataloader: 5000 +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.252 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:358 - Number of steps per epoch: 78 +(skyrl_entrypoint pid=391889) 2026-06-07 16:38:36.252 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:359 - Total training steps: 80 +(pid=2995275, ip=10.128.32.39) [2026-06-07 16:39:06,386 E 2995275 2995386] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.32.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 25x across cluster] +(pid=2662625, ip=10.128.32.46) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. +(pid=2662625, ip=10.128.32.46) [2026-06-07 16:39:07,218 E 2662625 2662665] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:20.342 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:20.343 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:20.344 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:21.623 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:21.875 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:21.875 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:21.875 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:21.875 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) 2026-06-07 16:40:21.925 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.28s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2697412, ip=10.128.32.43) (EngineCore_DP0 pid=2698009) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2697414, ip=10.128.32.43) (EngineCore_DP0 pid=2698001) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) [W607 16:40:24.572522745 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-43-interconnect-1.jupiter.internal]:60183 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) [W607 16:40:24.576008342 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) [rank0]:[W607 16:40:24.582010146 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) 2026-06-07 16:40:20.342 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) 2026-06-07 16:40:20.343 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) 2026-06-07 16:40:20.344 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:50.392 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:50.392 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(pid=2614410, ip=10.128.32.41) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=2614410, ip=10.128.32.41) No module named 'vllm._version' [repeated 3x across cluster] +(pid=2614410, ip=10.128.32.41) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:51.752 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:51.780 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:51.780 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:51.780 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:51.780 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:43:51.824 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.15s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:50.391 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:50.392 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:50.392 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) No module named 'vllm._version' [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:51.752 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:51.780 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:51.780 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:51.780 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:51.780 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:43:51.824 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.92s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) (EngineCore_DP0 pid=2577485) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2576837, ip=10.128.32.40) (EngineCore_DP0 pid=2577489) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) [W607 16:46:26.579304849 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-40-interconnect-1.jupiter.internal]:39565 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) [W607 16:46:26.580154880 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) [rank0]:[W607 16:46:26.586558911 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=2576837, ip=10.128.32.40) (EngineCore_DP0 pid=2577489) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:36.433 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:36.433 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) 2026-06-07 16:47:11.226 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1296 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:37.639 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:37.989 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:37.989 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:37.989 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:37.989 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) 2026-06-07 16:48:38.038 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.40s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:36.432 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:36.433 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:36.433 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:37.639 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:37.989 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:37.989 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:37.989 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:37.989 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) 2026-06-07 16:48:38.038 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.04s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739605, ip=10.128.32.34) (EngineCore_DP0 pid=2740434) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) (EngineCore_DP0 pid=2740438) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) [W607 16:49:22.529996202 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-34.jupiter.internal]:58359 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) [W607 16:49:22.533237515 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) [rank0]:[W607 16:49:22.539139353 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) 2026-06-07 16:52:06.306 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) 2026-06-07 16:52:06.306 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:04<00:12, 4.01s/it] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) 2026-06-07 16:52:07.518 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:06.306 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:06.306 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) Loading safetensors checkpoint shards: 50% Completed | 2/4 [00:08<00:08, 4.06s/it] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:07.518 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:07.791 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:07.791 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:07.791 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:07.791 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) 2026-06-07 16:52:07.835 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 1.87s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) (EngineCore_DP0 pid=2632450) +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.34s/it] [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) 2026-06-07 16:52:19.686 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1296 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 4x across cluster] +(pid=2994914, ip=10.128.32.39) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=2994914, ip=10.128.32.39) No module named 'vllm._version' +(pid=2994914, ip=10.128.32.39) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) 2026-06-07 16:52:19.639 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1296 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 3x across cluster] +(pid=2995275, ip=10.128.32.39) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=2995275, ip=10.128.32.39) No module named 'vllm._version' +(pid=2995275, ip=10.128.32.39) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) _C._set_float32_matmul_precision(precision) +(pid=2995274, ip=10.128.32.39) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 2x across cluster] +(pid=2995274, ip=10.128.32.39) No module named 'vllm._version' [repeated 2x across cluster] +(pid=2995274, ip=10.128.32.39) from .version import __version__, __version_tuple__ # isort:skip [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) (EngineCore_DP0 pid=3012342) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) (EngineCore_DP0 pid=3012343) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) [W607 16:54:12.735426276 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-44.jupiter.internal]:48903 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) [W607 16:54:12.739040171 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) [rank0]:[W607 16:54:12.745053709 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:38.697 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:38.697 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) [W607 16:55:33.476236797 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-38-interconnect-1.jupiter.internal]:41289 (errno: 97 - Address family not supported by protocol). [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) [W607 16:55:33.479705003 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) [rank0]:[W607 16:55:33.484923505 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:40.047 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:40.076 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:40.077 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:40.077 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:40.077 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) 2026-06-07 16:55:40.144 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.39s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:38.696 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:38.697 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:162 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:38.697 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:165 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:40.048 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:40.076 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:40.076 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:613 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:40.076 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:621 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:40.076 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1187 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) 2026-06-07 16:55:40.144 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1239 - Engine startup stagger: sleeping 2.33s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2831685, ip=10.128.32.45) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2831685, ip=10.128.32.45) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2831685, ip=10.128.32.45) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00) + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result + return self.__get_result() + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result + raise self._exception + ^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/workers/worker.py", line 149, in init_worker_process_group + torch.distributed.init_process_group( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 95, in wrapper + func_return = func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py", line 1762, in init_process_group + store, rank, world_size = next(rendezvous_iterator) + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 278, in _env_rendezvous_handler + store = _create_c10d_store( + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 198, in _create_c10d_store + return TCPStore( + ^^^^^^^^^ +torch.distributed.DistStoreError: Timed out after 601 seconds waiting for clients. 4/8 clients joined. +Exception raised from waitForWorkers at /pytorch/torch/csrc/distributed/c10d/TCPStore.cpp:396 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0xb0 (0x4000bf09c700 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5e9c9c0 (0x400ae12fc9c0 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: c10d::TCPStore::waitForWorkers() + 0x350 (0x400ae1391410 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: c10d::TCPStore::TCPStore(std::__cxx11::basic_string, std::allocator >, c10d::TCPStoreOptions const&) + 0x468 (0x400ae13918c8 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: + 0x109a094 (0x4000be86a094 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #5: + 0x113236c (0x4000be90236c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #6: + 0x5d6d60 (0x4000bdda6d60 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #7: + 0x1b7a38 (0xaaaacb007a38 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #8: _PyObject_MakeTpCall + 0x98 (0xaaaacafb5db8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #9: + 0x169f50 (0xaaaacafb9f50 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #10: + 0x1682e4 (0xaaaacafb82e4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #11: + 0x1e0ce8 (0xaaaacb030ce8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #12: + 0x1d7ddc (0xaaaacb027ddc in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #13: + 0x646b0c (0x4000bde16b0c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #14: _PyObject_MakeTpCall + 0x98 (0xaaaacafb5db8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #15: _PyEval_EvalFrameDefault + 0x280c (0xaaaacb0bae54 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #16: + 0x1808c0 (0xaaaacafd08c0 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #17: + 0x182bf8 (0xaaaacafd2bf8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #18: + 0x25fd30 (0xaaaacb0afd30 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #19: + 0x1b7d20 (0xaaaacb007d20 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #20: PyObject_Vectorcall + 0x54 (0xaaaacafb60e4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #21: _PyEval_EvalFrameDefault + 0x280c (0xaaaacb0bae54 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #22: + 0x1808c0 (0xaaaacafd08c0 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #23: + 0x1835c8 (0xaaaacafd35c8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #24: + 0x84b708 (0x40000a80b708 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #25: + 0x865fe4 (0x40000a825fe4 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #26: + 0x823470 (0x40000a7e3470 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #27: + 0x830110 (0x40000a7f0110 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #28: + 0x174a60 (0xaaaacafc4a60 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #29: PyObject_VectorcallMethod + 0xa4 (0xaaaacafb6270 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #30: PyIter_Send + 0xbc (0xaaaacaf9b62c in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #31: + 0xaa04 (0x40000ca7aa04 in /e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/lib-dynload/_asyncio.cpython-312-aarch64-linux-gnu.so) +frame #32: + 0xbcc4 (0x40000ca7bcc4 in /e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/lib-dynload/_asyncio.cpython-312-aarch64-linux-gnu.so) +frame #33: _PyObject_MakeTpCall + 0x98 (0xaaaacafb5db8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #34: + 0x28cfac (0xaaaacb0dcfac in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #35: + 0x1b7b68 (0xaaaacb007b68 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #36: _PyEval_EvalFrameDefault + 0x52ac (0xaaaacb0bd8f4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #37: + 0x83c564 (0x40000a7fc564 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #38: _PyEval_EvalFrameDefault + 0x52ac (0xaaaacb0bd8f4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #39: + 0x169f88 (0xaaaacafb9f88 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #40: + 0x36002c (0xaaaacb1b002c in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #41: + 0x2e4024 (0xaaaacb134024 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #42: + 0x80e00 (0x400009730e00 in /lib64/libc.so.6) +frame #43: + 0xeb49c (0x40000979b49c in /lib64/libc.so.6) +2026-06-07 17:12:36.033 | INFO | __main__:main:137 - Shutting down Ray on head node... +(pid=394710) [2026-06-07 17:03:06,091 E 394710 394918] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 6x across cluster] +(FSDPPolicyWorkerBase pid=2663495, ip=10.128.32.46) [W607 17:03:10.575118569 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-041-46.jupiter.internal]:41111 (errno: 97 - Address family not supported by protocol). [repeated 2x across cluster] +(FSDPPolicyWorkerBase pid=2663495, ip=10.128.32.46) [W607 17:03:10.575405606 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 2x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] Started monitoring (every 120s) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:02:41] OK: 50 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:02:41] OK: RSS 1.38 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(skyrl_entrypoint pid=391889) ⚙️ Running in WANDB offline mode +(skyrl_entrypoint pid=391889) [fd-monitor] [16:04:41] OK: 50 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:04:41] OK: RSS 1.50 GiB | node mem 161.4/858.0 GiB used (18.8%), avail 696.6 GiB +(skyrl_entrypoint pid=391889) INFO 06-07 16:06:28 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(skyrl_entrypoint pid=391889) [fd-monitor] [16:06:41] OK: 51 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:06:41] OK: RSS 1.59 GiB | node mem 161.5/858.0 GiB used (18.8%), avail 696.5 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:08:41] OK: 62 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:08:41] OK: RSS 1.63 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 696.9 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:10:41] OK: 62 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:10:41] OK: RSS 1.63 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 696.9 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:12:41] OK: 63 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:12:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.8 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:14:41] OK: 64 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:14:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:16 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(skyrl_entrypoint pid=391889) [fd-monitor] [16:16:41] OK: 64 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:16:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) WARNING 06-07 16:16:48 [arg_utils.py:1256] The global random seed is set to 42. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:48 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:48 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:48 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:48 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:48 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) WARNING 06-07 16:16:48 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:16:48 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) WARNING 06-07 16:16:48 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) WARNING 06-07 16:16:48 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) INFO 06-07 16:17:22 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:17:23 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=42, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:18 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:19 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.42:48887 backend=nccl +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:19 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:19 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:20 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:28 [default_loader.py:293] Loading weights took 8.20 seconds +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:28 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 8.778860 seconds +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:31 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:31 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:31 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:31 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:31 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.54 seconds +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) WARNING 06-07 16:18:32 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:32 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) WARNING 06-07 16:18:32 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) (EngineCore_DP0 pid=3114084) INFO 06-07 16:18:32 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3113603, ip=10.128.32.42) WARNING 06-07 16:18:32 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:35 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) WARNING 06-07 16:18:39 [arg_utils.py:1256] The global random seed is set to 56. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:39 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:39 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:39 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:39 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) WARNING 06-07 16:18:39 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:39 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:39 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) WARNING 06-07 16:18:40 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) WARNING 06-07 16:18:40 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) INFO 06-07 16:18:40 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) INFO 06-07 16:18:40 [vllm.py:846] Cudagraph is disabled under eager mode +(pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:35 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 2x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] [16:18:41] OK: 68 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:18:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) WARNING 06-07 16:18:40 [arg_utils.py:1256] The global random seed is set to 57. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:40 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:40 [model.py:1549] Using max model len 32768 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:40 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:40 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) WARNING 06-07 16:18:40 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) WARNING 06-07 16:18:40 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) WARNING 06-07 16:18:40 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:40 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:40 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) INFO 06-07 16:18:48 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) INFO 06-07 16:18:48 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:48 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=56, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:51 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:52 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.42:44549 backend=nccl +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:52 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:53 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) INFO 06-07 16:18:48 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:53 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) INFO 06-07 16:18:49 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=55, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:58 [default_loader.py:293] Loading weights took 4.13 seconds +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) INFO 06-07 16:18:52 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) INFO 06-07 16:18:53 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.42:51575 backend=nccl [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) INFO 06-07 16:18:53 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) INFO 06-07 16:18:53 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:18:58 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 4.877260 seconds +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:00 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:00 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:00 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=3114112, ip=10.128.32.42) (EngineCore_DP0 pid=3114390) INFO 06-07 16:18:53 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:00 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:00 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.44 seconds +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) WARNING 06-07 16:19:01 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:01 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) WARNING 06-07 16:19:01 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) (EngineCore_DP0 pid=3114382) INFO 06-07 16:19:01 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3114113, ip=10.128.32.42) WARNING 06-07 16:19:01 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(skyrl_entrypoint pid=391889) [fd-monitor] [16:20:41] OK: 68 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:20:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:18:58 [default_loader.py:293] Loading weights took 4.38 seconds [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:18:58 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 5.087965 seconds [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.46 seconds [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) WARNING 06-07 16:19:01 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) WARNING 06-07 16:19:01 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) (EngineCore_DP0 pid=3114394) INFO 06-07 16:19:01 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3114114, ip=10.128.32.42) WARNING 06-07 16:19:01 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 2x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] [16:22:41] OK: 70 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:22:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.8 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:24:41] OK: 72 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:24:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:26:41] OK: 72 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:26:41] OK: RSS 1.63 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 696.9 GiB +(pid=2697284, ip=10.128.32.43) INFO 06-07 16:28:12 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(skyrl_entrypoint pid=391889) [fd-monitor] [16:28:41] OK: 74 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:28:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(pid=2697413, ip=10.128.32.43) INFO 06-07 16:28:12 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) WARNING 06-07 16:29:10 [arg_utils.py:1256] The global random seed is set to 50. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:29:11 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:29:11 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:29:11 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:29:11 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:29:11 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) WARNING 06-07 16:29:11 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:29:11 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) WARNING 06-07 16:29:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) WARNING 06-07 16:29:11 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(skyrl_entrypoint pid=391889) [fd-monitor] [16:30:41] OK: 75 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:30:41] OK: RSS 1.63 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) WARNING 06-07 16:29:10 [arg_utils.py:1256] The global random seed is set to 53. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:29:11 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:29:11 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:29:11 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:29:11 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:29:11 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) WARNING 06-07 16:29:11 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:29:11 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) WARNING 06-07 16:29:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) WARNING 06-07 16:29:11 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] [16:32:41] OK: 80 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:32:41] OK: RSS 1.63 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 697.0 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [16:34:41] OK: 80 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:34:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) INFO 06-07 16:35:54 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:35:55 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=50, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(skyrl_entrypoint pid=391889) [fd-monitor] [16:36:41] OK: 80 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:36:41] OK: RSS 1.63 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) INFO 06-07 16:35:54 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:35:55 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=53, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:45 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:45 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) WARNING 06-07 16:37:50 [arg_utils.py:1256] The global random seed is set to 63. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(pid=2576838, ip=10.128.32.40) INFO 06-07 16:37:45 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:50 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:50 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:50 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:50 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:50 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) WARNING 06-07 16:37:50 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:37:50 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) WARNING 06-07 16:37:50 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) WARNING 06-07 16:37:50 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(skyrl_entrypoint pid=391889) [fd-monitor] [16:38:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:38:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) WARNING 06-07 16:37:51 [arg_utils.py:1256] The global random seed is set to 62. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:51 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:51 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:51 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:51 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:51 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) WARNING 06-07 16:37:51 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:37:51 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) WARNING 06-07 16:37:51 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) WARNING 06-07 16:37:51 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(pid=2739368, ip=10.128.32.34) INFO 06-07 16:39:00 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:23 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(pid=2739604, ip=10.128.32.34) INFO 06-07 16:39:00 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) WARNING 06-07 16:40:24 [arg_utils.py:1256] The global random seed is set to 58. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:24 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.43:60183 backend=nccl +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:40:24 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:40:24 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:24 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:40:24 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:40:24 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:40:24 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) WARNING 06-07 16:40:24 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:40:24 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) WARNING 06-07 16:40:24 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) WARNING 06-07 16:40:24 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:25 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:27 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:41 [default_loader.py:293] Loading weights took 13.76 seconds +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:23 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) WARNING 06-07 16:40:24 [arg_utils.py:1256] The global random seed is set to 60. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:24 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.43:46619 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) INFO 06-07 16:40:24 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) INFO 06-07 16:40:24 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:24 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) INFO 06-07 16:40:24 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) INFO 06-07 16:40:24 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) INFO 06-07 16:40:24 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) WARNING 06-07 16:40:24 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) INFO 06-07 16:40:24 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) WARNING 06-07 16:40:25 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) WARNING 06-07 16:40:25 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:25 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:27 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] [16:40:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:40:41] OK: RSS 1.64 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:41 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.544833 seconds +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:44 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:44 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:44 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2697412, ip=10.128.32.43) (EngineCore_DP0 pid=2698009) INFO 06-07 16:40:44 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) INFO 06-07 16:40:44 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.82 seconds +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) (EngineCore_DP0 pid=2698000) WARNING 06-07 16:40:45 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2697284, ip=10.128.32.43) WARNING 06-07 16:40:45 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(skyrl_entrypoint pid=391889) [fd-monitor] [16:42:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:42:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:41 [default_loader.py:293] Loading weights took 13.76 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:45 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:45 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) WARNING 06-07 16:40:45 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:41 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.540163 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:44 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:44 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:44 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:44 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) INFO 06-07 16:40:44 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.85 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) (EngineCore_DP0 pid=2698005) WARNING 06-07 16:40:45 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2697413, ip=10.128.32.43) WARNING 06-07 16:40:45 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(pid=2631427, ip=10.128.32.36) INFO 06-07 16:43:22 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) WARNING 06-07 16:43:53 [arg_utils.py:1256] The global random seed is set to 79. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:22 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) INFO 06-07 16:43:53 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) INFO 06-07 16:43:53 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) INFO 06-07 16:43:53 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) INFO 06-07 16:43:53 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) INFO 06-07 16:43:53 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) WARNING 06-07 16:43:53 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) INFO 06-07 16:43:53 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) WARNING 06-07 16:43:54 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2631819, ip=10.128.32.36) WARNING 06-07 16:43:54 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) WARNING 06-07 16:43:54 [arg_utils.py:1256] The global random seed is set to 81. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) INFO 06-07 16:44:08 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:54 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:54 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:54 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:54 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:54 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) WARNING 06-07 16:43:54 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:43:54 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) WARNING 06-07 16:43:55 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) WARNING 06-07 16:43:55 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) INFO 06-07 16:44:08 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:44:09 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=62, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(skyrl_entrypoint pid=391889) [fd-monitor] [16:44:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:44:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) INFO 06-07 16:44:08 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:44:09 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=65, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:46:25 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:46:26 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.40:39565 backend=nccl +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:46:26 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:46:26 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(skyrl_entrypoint pid=391889) [fd-monitor] [16:46:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:46:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:46:25 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:46:26 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.40:52891 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:46:26 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:46:26 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576837, ip=10.128.32.40) (EngineCore_DP0 pid=2577489) INFO 06-07 16:46:53 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:07 [default_loader.py:293] Loading weights took 13.09 seconds +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:46:54 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:07 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 40.482660 seconds +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:10 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:10 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:10 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2576837, ip=10.128.32.40) (EngineCore_DP0 pid=2577489) INFO 06-07 16:47:10 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:10 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.84 seconds +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) WARNING 06-07 16:47:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:11 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) WARNING 06-07 16:47:11 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) (EngineCore_DP0 pid=2577500) INFO 06-07 16:47:11 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2576836, ip=10.128.32.40) WARNING 06-07 16:47:11 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) INFO 06-07 16:47:33 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:07 [default_loader.py:293] Loading weights took 13.09 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:07 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 40.481976 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:10 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:10 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:10 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:10 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:10 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.86 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) WARNING 06-07 16:47:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:11 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) WARNING 06-07 16:47:11 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576838, ip=10.128.32.40) (EngineCore_DP0 pid=2577495) INFO 06-07 16:47:11 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2576535, ip=10.128.32.40) WARNING 06-07 16:47:11 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:47:34 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=58, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(pid=3010613, ip=10.128.32.44) INFO 06-07 16:48:08 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:47:34 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=61, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) INFO 06-07 16:48:26 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:48:27 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=66, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) WARNING 06-07 16:48:40 [arg_utils.py:1256] The global random seed is set to 68. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) INFO 06-07 16:48:26 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:48:27 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=81, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) INFO 06-07 16:48:40 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) INFO 06-07 16:48:40 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) INFO 06-07 16:48:40 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) INFO 06-07 16:48:40 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) INFO 06-07 16:48:40 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) WARNING 06-07 16:48:40 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) INFO 06-07 16:48:40 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) WARNING 06-07 16:48:40 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) WARNING 06-07 16:48:40 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(skyrl_entrypoint pid=391889) [fd-monitor] [16:48:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:48:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:49:22 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) WARNING 06-07 16:48:40 [arg_utils.py:1256] The global random seed is set to 69. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) INFO 06-07 16:48:40 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) INFO 06-07 16:48:40 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) INFO 06-07 16:48:40 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) INFO 06-07 16:48:40 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) INFO 06-07 16:48:40 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) WARNING 06-07 16:48:40 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) INFO 06-07 16:48:40 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) WARNING 06-07 16:48:40 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011432, ip=10.128.32.44) WARNING 06-07 16:48:40 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:49:22 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.34:58359 backend=nccl +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:49:22 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:15 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:49:22 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:49:22 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.34:58341 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:49:22 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:16 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:29 [default_loader.py:293] Loading weights took 13.02 seconds +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:15 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:16 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:29 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.171795 seconds +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:32 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:32 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:32 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:32 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:32 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.81 seconds +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) WARNING 06-07 16:50:33 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:33 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) WARNING 06-07 16:50:33 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) (EngineCore_DP0 pid=2740424) INFO 06-07 16:50:33 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2739368, ip=10.128.32.34) WARNING 06-07 16:50:33 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(skyrl_entrypoint pid=391889) [fd-monitor] [16:50:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:50:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:29 [default_loader.py:293] Loading weights took 12.88 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:29 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.175118 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:32 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:32 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:32 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) (EngineCore_DP0 pid=2740428) INFO 06-07 16:50:32 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) (EngineCore_DP0 pid=2740438) INFO 06-07 16:50:32 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.83 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) (EngineCore_DP0 pid=2740438) WARNING 06-07 16:50:33 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) (EngineCore_DP0 pid=2740438) INFO 06-07 16:50:33 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) (EngineCore_DP0 pid=2740438) WARNING 06-07 16:50:33 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739603, ip=10.128.32.34) (EngineCore_DP0 pid=2740438) INFO 06-07 16:50:33 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2739604, ip=10.128.32.34) WARNING 06-07 16:50:33 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:50:42 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:50:43 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.36:48845 backend=nccl +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:50:43 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:51:09 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:50:43 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:50:43 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.36:42177 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:50:43 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(pid=2705575, ip=10.128.32.38) INFO 06-07 16:51:38 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:51:09 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) INFO 06-07 16:51:54 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:51:56 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=67, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:02 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) INFO 06-07 16:51:54 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:51:56 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=78, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) WARNING 06-07 16:52:09 [arg_utils.py:1256] The global random seed is set to 72. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:02 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) INFO 06-07 16:52:09 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) INFO 06-07 16:52:09 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) INFO 06-07 16:52:09 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) INFO 06-07 16:52:09 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) INFO 06-07 16:52:09 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) WARNING 06-07 16:52:09 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) INFO 06-07 16:52:09 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) WARNING 06-07 16:52:10 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2705804, ip=10.128.32.38) WARNING 06-07 16:52:10 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:15 [default_loader.py:293] Loading weights took 13.37 seconds +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) WARNING 06-07 16:52:10 [arg_utils.py:1256] The global random seed is set to 70. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:52:10 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:52:10 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:52:10 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:52:10 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:52:10 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) WARNING 06-07 16:52:10 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:52:10 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) WARNING 06-07 16:52:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) WARNING 06-07 16:52:11 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:16 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 65.938186 seconds +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:52:18 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:52:18 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:52:18 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2631821, ip=10.128.32.36) (EngineCore_DP0 pid=2632466) INFO 06-07 16:52:18 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:18 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.81 seconds +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) WARNING 06-07 16:52:19 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) WARNING 06-07 16:52:19 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(skyrl_entrypoint pid=391889) [fd-monitor] [16:52:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:52:41] OK: RSS 1.64 GiB | node mem 161.0/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:15 [default_loader.py:293] Loading weights took 13.37 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:19 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:19 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) WARNING 06-07 16:52:19 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:16 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 65.945630 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:18 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:18 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631427, ip=10.128.32.36) (EngineCore_DP0 pid=2632455) INFO 06-07 16:52:18 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:18 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) INFO 06-07 16:52:18 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.79 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) (EngineCore_DP0 pid=2632471) WARNING 06-07 16:52:19 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2631820, ip=10.128.32.36) WARNING 06-07 16:52:19 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) INFO 06-07 16:53:15 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:53:17 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=70, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:54:12 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) INFO 06-07 16:53:15 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:53:17 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=73, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:54:12 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.44:48903 backend=nccl +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:54:12 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:54:12 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(pid=2831550, ip=10.128.32.45) INFO 06-07 16:54:18 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:54:12 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.44:53241 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:54:12 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(pid=2831684, ip=10.128.32.45) INFO 06-07 16:54:18 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(skyrl_entrypoint pid=391889) [fd-monitor] [16:54:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:54:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(pid=2831686, ip=10.128.32.45) INFO 06-07 16:54:18 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:04 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:06 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:19 [default_loader.py:293] Loading weights took 13.56 seconds +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:04 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:06 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:20 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.714728 seconds +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:22 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:22 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:22 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:22 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:22 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.82 seconds +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) WARNING 06-07 16:55:23 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:23 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) WARNING 06-07 16:55:23 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) (EngineCore_DP0 pid=3012340) INFO 06-07 16:55:23 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3010613, ip=10.128.32.44) WARNING 06-07 16:55:23 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:55:32 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:19 [default_loader.py:293] Loading weights took 13.43 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:20 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.718624 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) (EngineCore_DP0 pid=3012342) INFO 06-07 16:55:22 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) (EngineCore_DP0 pid=3012342) INFO 06-07 16:55:22 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011433, ip=10.128.32.44) (EngineCore_DP0 pid=3012342) INFO 06-07 16:55:22 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:22 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:22 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.81 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) WARNING 06-07 16:55:23 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:23 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) WARNING 06-07 16:55:23 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) (EngineCore_DP0 pid=3012341) INFO 06-07 16:55:23 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3011671, ip=10.128.32.44) WARNING 06-07 16:55:23 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:55:33 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.38:39711 backend=nccl +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:55:33 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) WARNING 06-07 16:55:42 [arg_utils.py:1256] The global random seed is set to 82. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:55:32 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:55:33 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.38:41289 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:55:33 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:56:00 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2831685, ip=10.128.32.45) WARNING 06-07 16:55:42 [arg_utils.py:1256] The global random seed is set to 84. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:09 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:09 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:09 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:09 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:09 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) WARNING 06-07 16:56:09 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:09 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:56:00 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) WARNING 06-07 16:56:09 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) WARNING 06-07 16:56:09 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(skyrl_entrypoint pid=391889) [fd-monitor] [16:56:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:56:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:09 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:09 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:09 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:09 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:09 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) WARNING 06-07 16:56:09 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:09 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) WARNING 06-07 16:56:09 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) WARNING 06-07 16:56:09 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) INFO 06-07 16:56:50 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 16:56:52 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=82, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:56:54 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:08 [default_loader.py:293] Loading weights took 13.39 seconds +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) INFO 06-07 16:56:50 [pynccl.py:178] pynccl trace buffer enabled: size=10000 dump_dir=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps flush_interval=0 s [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 16:56:52 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=85, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:56:54 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 67.984083 seconds +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:11 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:11 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:11 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:11 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:11 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.83 seconds +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) WARNING 06-07 16:57:12 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:12 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) WARNING 06-07 16:57:12 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) (EngineCore_DP0 pid=2706759) INFO 06-07 16:57:12 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2705575, ip=10.128.32.38) WARNING 06-07 16:57:12 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(skyrl_entrypoint pid=391889) [fd-monitor] [16:58:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [16:58:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:08 [default_loader.py:293] Loading weights took 13.39 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 67.996617 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:11 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:11 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:11 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:11 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:11 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.82 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) WARNING 06-07 16:57:12 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:12 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) WARNING 06-07 16:57:12 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) (EngineCore_DP0 pid=2706739) INFO 06-07 16:57:12 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2705803, ip=10.128.32.38) WARNING 06-07 16:57:12 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 16:59:59 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:00:00 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.45:41873 backend=nccl +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:00:00 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(skyrl_entrypoint pid=391889) [fd-monitor] [17:00:41] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [17:00:41] OK: RSS 1.64 GiB | node mem 161.1/858.0 GiB used (18.8%), avail 696.9 GiB +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 16:59:59 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:00:00 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.32.45:60113 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:00:00 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:00:52 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:00:53 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:07 [default_loader.py:293] Loading weights took 13.57 seconds +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:00:52 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:00:53 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.159333 seconds +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:10 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:10 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:10 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=2831684, ip=10.128.32.45) (EngineCore_DP0 pid=2832749) INFO 06-07 17:01:10 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:10 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.83 seconds +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) WARNING 06-07 17:01:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:11 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) WARNING 06-07 17:01:11 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) (EngineCore_DP0 pid=2832747) INFO 06-07 17:01:11 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=2831684, ip=10.128.32.45) WARNING 06-07 17:01:11 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(pid=2662625, ip=10.128.32.46) ⚙️ Running in WANDB offline mode +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:07 [default_loader.py:293] Loading weights took 13.57 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:08 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.161752 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:10 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:10 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:10 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:10 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:10 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.84 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) WARNING 06-07 17:01:11 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:11 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) WARNING 06-07 17:01:11 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831686, ip=10.128.32.45) (EngineCore_DP0 pid=2832741) INFO 06-07 17:01:11 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2831550, ip=10.128.32.45) WARNING 06-07 17:01:11 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] [17:02:41] OK: 86 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [17:02:41] OK: RSS 1.64 GiB | node mem 162.3/858.0 GiB used (18.9%), avail 695.7 GiB +(pid=2663493, ip=10.128.32.46) ⚙️ Running in WANDB offline mode +(skyrl_entrypoint pid=391889) [fd-monitor] [17:04:41] OK: 89 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [17:04:41] OK: RSS 1.64 GiB | node mem 162.4/858.0 GiB used (18.9%), avail 695.6 GiB +(pid=2663495, ip=10.128.32.46) ⚙️ Running in WANDB offline mode [repeated 2x across cluster] +(skyrl_entrypoint pid=391889) [fd-monitor] [17:06:41] OK: 89 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [17:06:41] OK: RSS 1.64 GiB | node mem 162.4/858.0 GiB used (18.9%), avail 695.5 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [17:08:41] OK: 83 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [17:08:41] OK: RSS 1.64 GiB | node mem 162.4/858.0 GiB used (18.9%), avail 695.6 GiB +(skyrl_entrypoint pid=391889) [fd-monitor] [17:10:41] OK: 83 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=391889) [fd-monitor] [17:10:41] OK: RSS 1.64 GiB | node mem 162.5/858.0 GiB used (18.9%), avail 695.5 GiB +Error executing job with overrides: ['+terminal_bench_config=terminal_bench', 'trainer.strategy=fsdp2', 'trainer.algorithm.advantage_estimator=rloo_n', 'trainer.algorithm.use_kl_loss=false', 'trainer.algorithm.kl_loss_coef=0.0', 'trainer.algorithm.eps_clip_low=0.2', 'trainer.algorithm.eps_clip_high=0.05', 'trainer.algorithm.loss_reduction=sequence_mean', 'trainer.epochs=2', 'trainer.max_steps=80', 'trainer.update_epochs_per_batch=1', 'trainer.train_batch_size=64', 'trainer.policy_mini_batch_size=64', 'trainer.eval_batch_size=64', 'trainer.micro_forward_batch_size_per_gpu=4', 'trainer.micro_train_batch_size_per_gpu=1', 'trainer.max_prompt_length=999999', 'trainer.eval_interval=999999', 'trainer.eval_before_train=false', 'trainer.ckpt_interval=2', 'trainer.resume_mode=latest', 'trainer.hf_save_interval=5', '++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0', '++trainer.hf_hub_private=false', '++trainer.hf_hub_revision=main', '++trainer.enable_db_registration=false', 'trainer.project_name=OpenThoughts-Agent', 'trainer.log_level=INFO', 'trainer.tracker_commit_each_step=true', 'trainer.logger=console', 'trainer.run_name=ablation-pymethods2test-seqmean-arm0', 'trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints', 'trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/exports', 'trainer.policy.optimizer_config.lr=8e-6', 'trainer.policy.optimizer_config.weight_decay=0.0', 'trainer.policy.optimizer_config.adam_betas=[0.9,0.999]', 'trainer.policy.optimizer_config.max_grad_norm=0.9', 'trainer.policy.fsdp_config.cpu_offload=false', 'trainer.policy.fsdp_config.reshard_after_forward=true', 'trainer.policy.fsdp_config.fsdp_size=4', 'trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'trainer.ref.fsdp_config.cpu_offload=false', 'trainer.ref.fsdp_config.reshard_after_forward=true', 'trainer.ref.fsdp_config.fsdp_size=4', 'trainer.placement.colocate_all=false', 'trainer.placement.policy_num_nodes=2', 'trainer.placement.ref_num_nodes=2', 'trainer.placement.policy_num_gpus_per_node=4', 'trainer.placement.ref_num_gpus_per_node=4', 'trainer.fully_async.max_staleness_steps=16', 'trainer.fully_async.num_parallel_generation_workers=338', 'generator.backend=vllm', 'generator.timeout_multiplier=1.0', 'generator.model_dtype=bfloat16', 'generator.inference_engine_tensor_parallel_size=1', 'generator.num_inference_engines=48', 'generator.n_samples_per_prompt=8', 'generator.eval_n_samples_per_prompt=8', 'generator.gpu_memory_utilization=0.75', 'generator.max_num_seqs=24', 'generator.max_num_batched_tokens=65536', 'generator.enable_prefix_caching=true', 'generator.enable_chunked_prefill=true', 'generator.run_engines_locally=true', 'generator.weight_sync_backend=nccl', 'generator.async_engine=true', 'generator.batched=false', 'generator.enable_http_endpoint=true', 'generator.enable_ray_prometheus_stats=false', 'generator.vllm_stats_interval=1', 'generator.append_eos_token_after_stop_str_in_multi_turn=true', 'generator.max_turns=999999', 'generator.sampling_params.max_generate_length=4096', 'generator.sampling_params.temperature=0.7', 'generator.sampling_params.top_p=0.95', 'generator.sampling_params.top_k=20', '++generator.engine_init_kwargs.max_model_len=32768', '++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2', '++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"]', 'data.val_data=[]', '+terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/trace_jobs', '+terminal_bench_config.harbor.name=terminus-2', '+terminal_bench_config.harbor.max_episodes=999999', '+terminal_bench_config.harbor.enable_summarize=false', '+terminal_bench_config.harbor.store_all_messages=true', '+terminal_bench_config.harbor.trajectory_config.raw_content=true', '+terminal_bench_config.harbor.enable_episode_logging=false', '+terminal_bench_config.harbor.record_terminal_session=false', '+terminal_bench_config.harbor.enable_pane_logging=false', '+terminal_bench_config.harbor.strict_json_parser=true', '+terminal_bench_config.harbor.interleaved_thinking=true', '+terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true', '+terminal_bench_config.harbor.override_timeout_sec=900', '+terminal_bench_config.harbor.override_cpus=1', '+terminal_bench_config.harbor.override_memory_mb=2048', '+terminal_bench_config.harbor.override_storage_mb=2048', '+terminal_bench_config.harbor.auto_snapshot=true', '+terminal_bench_config.harbor.verifier_override_timeout_sec=120', '+terminal_bench_config.harbor.max_retries=3', '+terminal_bench_config.harbor.min_wait_sec=60.0', '+terminal_bench_config.harbor.max_wait_sec=600.0', '+terminal_bench_config.harbor.wait_multiplier=2.0', '+terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"]', '+terminal_bench_config.harbor.n_concurrent_trials=675', '+terminal_bench_config.harbor.log_level=INFO', '+terminal_bench_config.harbor.enable_reward_shaping=false', '+terminal_bench_config.harbor.enable_error_classification=true', '+terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"]', '+terminal_bench_config.harbor.default_error_treatment=zero', '+terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"]', '+terminal_bench_config.harbor.zero_exceptions=[]', '+terminal_bench_config.model_info.max_input_tokens=32000', '+terminal_bench_config.model_info.max_output_tokens=4096', '+terminal_bench_config.archiving.enabled=false', '+terminal_bench_config.trace_upload.enabled=true', '+terminal_bench_config.trace_upload.repo_org=DCAgent', '+terminal_bench_config.trace_upload.episodes=last', '+terminal_bench_config.trace_upload.dataset_type=SFT', '+terminal_bench_config.trace_upload.cleanup=true'] +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 142, in + main() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/main.py", line 94, in decorated_main + _run_hydra( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra + _run_app( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 457, in _run_app + run_and_report( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 223, in run_and_report + raise ex + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 220, in run_and_report + return func() + ^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 458, in + lambda: hydra.run( + ^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/hydra.py", line 132, in run + _ = ret.return_value + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 260, in return_value + raise self._return_value + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 186, in run_job + ret.return_value = task_function(task_cfg) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 132, in main + ray.get(skyrl_entrypoint.remote(cfg)) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 2967, in get + values, debugger_breakpoint = worker.get_objects( + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 1015, in get_objects + raise value.as_instanceof_cause() +ray.exceptions.RayTaskError(DistStoreError): ray::skyrl_entrypoint() (pid=391889, ip=10.128.32.33) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 105, in skyrl_entrypoint + exp.run() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/entrypoints/main_base.py", line 481, in run + trainer = self._setup_trainer() + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/entrypoints/main_base.py", line 450, in _setup_trainer + trainer.build_models(PolicyWorker, CriticWorker, RefWorker, policy_pg=self.policy_pg) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/trainer.py", line 768, in build_models + policy_model = PPORayActorGroup( + ^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/workers/worker.py", line 511, in __init__ + self._initiate_actors(pg, num_gpus_per_actor) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/workers/worker.py", line 637, in _initiate_actors + ray.get([actor.init_worker_process_group.remote() for actor in self._actor_handlers]) + ^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^ +ray.exceptions.RayTaskError(DistStoreError): ray::FSDPPolicyWorkerBase.init_worker_process_group() (pid=2662625, ip=10.128.32.46, actor_id=b5939e2b137362308272f3e202000000, repr=) + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 449, in result + return self.__get_result() + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result + raise self._exception + ^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/skyrl_train/workers/worker.py", line 149, in init_worker_process_group + torch.distributed.init_process_group( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 95, in wrapper + func_return = func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py", line 1762, in init_process_group + store, rank, world_size = next(rendezvous_iterator) + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 278, in _env_rendezvous_handler + store = _create_c10d_store( + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/rendezvous.py", line 198, in _create_c10d_store + return TCPStore( + ^^^^^^^^^ +torch.distributed.DistStoreError: Timed out after 601 seconds waiting for clients. 4/8 clients joined. +Exception raised from waitForWorkers at /pytorch/torch/csrc/distributed/c10d/TCPStore.cpp:396 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0xb0 (0x4000bf09c700 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5e9c9c0 (0x400ae12fc9c0 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: c10d::TCPStore::waitForWorkers() + 0x350 (0x400ae1391410 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: c10d::TCPStore::TCPStore(std::__cxx11::basic_string, std::allocator >, c10d::TCPStoreOptions const&) + 0x468 (0x400ae13918c8 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: + 0x109a094 (0x4000be86a094 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #5: + 0x113236c (0x4000be90236c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #6: + 0x5d6d60 (0x4000bdda6d60 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #7: + 0x1b7a38 (0xaaaacb007a38 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #8: _PyObject_MakeTpCall + 0x98 (0xaaaacafb5db8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #9: + 0x169f50 (0xaaaacafb9f50 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #10: + 0x1682e4 (0xaaaacafb82e4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #11: + 0x1e0ce8 (0xaaaacb030ce8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #12: + 0x1d7ddc (0xaaaacb027ddc in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #13: + 0x646b0c (0x4000bde16b0c in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #14: _PyObject_MakeTpCall + 0x98 (0xaaaacafb5db8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #15: _PyEval_EvalFrameDefault + 0x280c (0xaaaacb0bae54 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #16: + 0x1808c0 (0xaaaacafd08c0 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #17: + 0x182bf8 (0xaaaacafd2bf8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #18: + 0x25fd30 (0xaaaacb0afd30 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #19: + 0x1b7d20 (0xaaaacb007d20 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #20: PyObject_Vectorcall + 0x54 (0xaaaacafb60e4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #21: _PyEval_EvalFrameDefault + 0x280c (0xaaaacb0bae54 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #22: + 0x1808c0 (0xaaaacafd08c0 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #23: + 0x1835c8 (0xaaaacafd35c8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #24: + 0x84b708 (0x40000a80b708 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #25: + 0x865fe4 (0x40000a825fe4 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #26: + 0x823470 (0x40000a7e3470 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #27: + 0x830110 (0x40000a7f0110 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #28: + 0x174a60 (0xaaaacafc4a60 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #29: PyObject_VectorcallMethod + 0xa4 (0xaaaacafb6270 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #30: PyIter_Send + 0xbc (0xaaaacaf9b62c in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #31: + 0xaa04 (0x40000ca7aa04 in /e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/lib-dynload/_asyncio.cpython-312-aarch64-linux-gnu.so) +frame #32: + 0xbcc4 (0x40000ca7bcc4 in /e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/lib-dynload/_asyncio.cpython-312-aarch64-linux-gnu.so) +frame #33: _PyObject_MakeTpCall + 0x98 (0xaaaacafb5db8 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #34: + 0x28cfac (0xaaaacb0dcfac in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #35: + 0x1b7b68 (0xaaaacb007b68 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #36: _PyEval_EvalFrameDefault + 0x52ac (0xaaaacb0bd8f4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #37: + 0x83c564 (0x40000a7fc564 in /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_raylet.so) +frame #38: _PyEval_EvalFrameDefault + 0x52ac (0xaaaacb0bd8f4 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #39: + 0x169f88 (0xaaaacafb9f88 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #40: + 0x36002c (0xaaaacb1b002c in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #41: + 0x2e4024 (0xaaaacb134024 in ray::FSDPPolicyWorkerBase.init_worker_process_group) +frame #42: + 0x80e00 (0x400009730e00 in /lib64/libc.so.6) +frame #43: + 0xeb49c (0x40000979b49c in /lib64/libc.so.6) +Stopping Ray cluster... + Warning: Failed to stop Ray on jpbo-041-36: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs,TORCH_NCCL_TRACE_BUFFER_SIZE=10000,TORCH_FR_BUFFER_SIZE=10000,TORCH_NCCL_DESYNC_DEBUG=1,TORCH_NCCL_DEBUG_INFO_TEMP_FILE=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_PYNCCL_TRACE_BUFFER_SIZE=10000,VLLM_PYNCCL_TRACE_DUMP_DIR=/e/data1/datasets/playground/ot-baf/experiments/_nccl_dumps/nccl_trace,VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY=TORCH_NCCL_,TORCH_FR_,VLLM_PYNCCL_', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-041-36', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds +Ray cluster stopped +[RLJobRunner] Crash detected (exit!=0) — preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/ray_logs BEFORE trace upload (so a wall-clock kill can't lose crash evidence)... +[RLJobRunner] Crash-time Ray log preservation timed out (600s); continuing. +[RLJobRunner] Launching trace upload (training exit code: 1): + repo_id: DCAgent/ablation-pymethods2test-seqmean-arm0 + job_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0 + episodes: last + log: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/logs/ablation-pymethods2test-seqmean-arm0_trace_upload.log +[RLJobRunner] Waiting for trace upload to complete... +[RLJobRunner] Trace upload failed with exit code 1. +Preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ray_logs/ + Collecting Ray logs from worker jpbo-041-34... + Collecting Ray logs from worker jpbo-041-35... + Collecting Ray logs from worker jpbo-041-36... + Collecting Ray logs from worker jpbo-041-37... + Collecting Ray logs from worker jpbo-041-38... + Collecting Ray logs from worker jpbo-041-39... + Collecting Ray logs from worker jpbo-041-40... + Collecting Ray logs from worker jpbo-041-41... + Collecting Ray logs from worker jpbo-041-42... + Collecting Ray logs from worker jpbo-041-43... + Collecting Ray logs from worker jpbo-041-44... + Collecting Ray logs from worker jpbo-041-45... + Collecting Ray logs from worker jpbo-041-46... +Ray log preservation complete diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_630130.out b/training_logs/ablation-pymethods2test-seqmean-arm0_630130.out new file mode 100644 index 0000000..03da5d4 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_630130.out @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ccadea7fa55f83615cb7841c06afa1a0bae0a0f8f21a8e874cf3b36a35660724 +size 23994623 diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_657667.out b/training_logs/ablation-pymethods2test-seqmean-arm0_657667.out new file mode 100644 index 0000000..c243385 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_657667.out @@ -0,0 +1,17 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Warning: RL environment not found at /e/home/jusers/feuer1/jupiter/envs/rl +Run ./hpc/setup_rl_env.sh to create it, or set DCFT_RL_ENV +/usr/bin/which: no python in (/e/software/default/stages/2026/software/nvidia-compilers/25.9-CUDA-13/Linux_aarch64/25.9/compilers/bin:/e/software/default/stages/2026/software/numactl/2.0.19-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/GCCcore/14.3.0/bin:/e/software/default/stages/2026/software/CUDA/13/nvvm/bin:/e/software/default/stages/2026/software/CUDA/13/bin:/e/scratch/jureap59/feuer1/miniforge3/condabin:/e/software/default/stages/2026/software/make/4.4.1-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/binutils/2.44-GCCcore-14.3.0/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/jsc/bin:/usr/local/jsc/bin:/opt/parastation/bin:/e/software/default/bin) +Python executable: +/var/spool/slurmd/job657667/slurm_script: line 128: python: command not found +Python path check: +[ray] RAY_TMPDIR=/tmp/ray/ray_657667 +/var/spool/slurmd/job657667/slurm_script: line 253: /e/home/jusers/feuer1/jupiter/hpc/shell_utils/triton_cache.sh: No such file or directory diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_657668.out b/training_logs/ablation-pymethods2test-seqmean-arm0_657668.out new file mode 100644 index 0000000..b976648 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_657668.out @@ -0,0 +1,17 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Warning: RL environment not found at /e/home/jusers/feuer1/jupiter/envs/rl +Run ./hpc/setup_rl_env.sh to create it, or set DCFT_RL_ENV +/usr/bin/which: no python in (/e/software/default/stages/2026/software/nvidia-compilers/25.9-CUDA-13/Linux_aarch64/25.9/compilers/bin:/e/software/default/stages/2026/software/numactl/2.0.19-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/GCCcore/14.3.0/bin:/e/software/default/stages/2026/software/CUDA/13/nvvm/bin:/e/software/default/stages/2026/software/CUDA/13/bin:/e/scratch/jureap59/feuer1/miniforge3/condabin:/e/software/default/stages/2026/software/make/4.4.1-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/binutils/2.44-GCCcore-14.3.0/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/jsc/bin:/usr/local/jsc/bin:/opt/parastation/bin:/e/software/default/bin) +Python executable: +/var/spool/slurmd/job657668/slurm_script: line 128: python: command not found +Python path check: +[ray] RAY_TMPDIR=/tmp/ray/ray_657668 +/var/spool/slurmd/job657668/slurm_script: line 253: /e/home/jusers/feuer1/jupiter/hpc/shell_utils/triton_cache.sh: No such file or directory diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_657669.out b/training_logs/ablation-pymethods2test-seqmean-arm0_657669.out new file mode 100644 index 0000000..0ed23c3 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_657669.out @@ -0,0 +1,17 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Warning: RL environment not found at /e/home/jusers/feuer1/jupiter/envs/rl +Run ./hpc/setup_rl_env.sh to create it, or set DCFT_RL_ENV +/usr/bin/which: no python in (/e/software/default/stages/2026/software/nvidia-compilers/25.9-CUDA-13/Linux_aarch64/25.9/compilers/bin:/e/software/default/stages/2026/software/numactl/2.0.19-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/GCCcore/14.3.0/bin:/e/software/default/stages/2026/software/CUDA/13/nvvm/bin:/e/software/default/stages/2026/software/CUDA/13/bin:/e/scratch/jureap59/feuer1/miniforge3/condabin:/e/software/default/stages/2026/software/make/4.4.1-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/binutils/2.44-GCCcore-14.3.0/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/jsc/bin:/usr/local/jsc/bin:/opt/parastation/bin:/e/software/default/bin) +Python executable: +/var/spool/slurmd/job657669/slurm_script: line 128: python: command not found +Python path check: +[ray] RAY_TMPDIR=/tmp/ray/ray_657669 +/var/spool/slurmd/job657669/slurm_script: line 253: /e/home/jusers/feuer1/jupiter/hpc/shell_utils/triton_cache.sh: No such file or directory diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_657670.out b/training_logs/ablation-pymethods2test-seqmean-arm0_657670.out new file mode 100644 index 0000000..6d97ac5 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_657670.out @@ -0,0 +1,17 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Warning: RL environment not found at /e/home/jusers/feuer1/jupiter/envs/rl +Run ./hpc/setup_rl_env.sh to create it, or set DCFT_RL_ENV +/usr/bin/which: no python in (/e/software/default/stages/2026/software/nvidia-compilers/25.9-CUDA-13/Linux_aarch64/25.9/compilers/bin:/e/software/default/stages/2026/software/numactl/2.0.19-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/GCCcore/14.3.0/bin:/e/software/default/stages/2026/software/CUDA/13/nvvm/bin:/e/software/default/stages/2026/software/CUDA/13/bin:/e/scratch/jureap59/feuer1/miniforge3/condabin:/e/software/default/stages/2026/software/make/4.4.1-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/binutils/2.44-GCCcore-14.3.0/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/jsc/bin:/usr/local/jsc/bin:/opt/parastation/bin:/e/software/default/bin) +Python executable: +/var/spool/slurmd/job657670/slurm_script: line 128: python: command not found +Python path check: +[ray] RAY_TMPDIR=/tmp/ray/ray_657670 +/var/spool/slurmd/job657670/slurm_script: line 253: /e/home/jusers/feuer1/jupiter/hpc/shell_utils/triton_cache.sh: No such file or directory diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_657671.out b/training_logs/ablation-pymethods2test-seqmean-arm0_657671.out new file mode 100644 index 0000000..b326bf8 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_657671.out @@ -0,0 +1,17 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Warning: RL environment not found at /e/home/jusers/feuer1/jupiter/envs/rl +Run ./hpc/setup_rl_env.sh to create it, or set DCFT_RL_ENV +/usr/bin/which: no python in (/e/software/default/stages/2026/software/nvidia-compilers/25.9-CUDA-13/Linux_aarch64/25.9/compilers/bin:/e/software/default/stages/2026/software/numactl/2.0.19-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/GCCcore/14.3.0/bin:/e/software/default/stages/2026/software/CUDA/13/nvvm/bin:/e/software/default/stages/2026/software/CUDA/13/bin:/e/scratch/jureap59/feuer1/miniforge3/condabin:/e/software/default/stages/2026/software/make/4.4.1-GCCcore-14.3.0/bin:/e/software/default/stages/2026/software/binutils/2.44-GCCcore-14.3.0/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/jsc/bin:/usr/local/jsc/bin:/opt/parastation/bin:/e/software/default/bin) +Python executable: +/var/spool/slurmd/job657671/slurm_script: line 128: python: command not found +Python path check: +[ray] RAY_TMPDIR=/tmp/ray/ray_657671 +/var/spool/slurmd/job657671/slurm_script: line 253: /e/home/jusers/feuer1/jupiter/hpc/shell_utils/triton_cache.sh: No such file or directory diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_661733.out b/training_logs/ablation-pymethods2test-seqmean-arm0_661733.out new file mode 100644 index 0000000..922d327 --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_661733.out @@ -0,0 +1,364 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Activating RL environment: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl +Python executable: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python path check: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +[ray] RAY_TMPDIR=/tmp/ray/ray_661733 +[triton_cache] Triton cache: /tmp/triton_cache_feuer1_661733 +[triton_cache] TorchInductor cache: /tmp/torchinductor_cache_feuer1_661733 +[proxy] ✓ Found proxychains binary at /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 +[proxy] Setting up SSH tunnel to jpbl-s01-01 +[proxy] SSH key: /e/home/jusers/feuer1/jupiter/.ssh/authorized_keys/id_ed25519_jsc +[proxy] Tunnel port: 7003 +[proxy] Node IP: 10.128.16.35 (workers will connect here) +[proxy] ✓ SSH tunnel started successfully +[proxy] ✓ Generated proxychains config at /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_661733.conf +[proxy] - Internal traffic (10.x.x.x, 172.x.x.x, 169.254.x.x) → DIRECT +[proxy] - External traffic (internet) → PROXY via tunnel +[proxy] ✓ Daytona timeout settings configured +[proxy] Testing proxy connectivity... +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_661733.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[proxy] ✓ Proxy connectivity test passed (huggingface.co reachable via wrapped binary) +[proxy] ⚠ Tunnel not accessible at 10.128.16.35:7003 (workers may fail) +[proxy] ✓ Proxy setup complete (using wrapped binary for Ray workers) +[container_runtime] Using cloud backend: daytona (no local container setup) +=== Universal RL Training Runner === +Config: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/configs/ablation-pymethods2test-seqmean-arm0_rl_config.json +Working directory: /e/scratch/jureap59/feuer1/OpenThoughts-Agent +Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python version: Python 3.12.12 +UV_USE_IO_URING: 0 +Proxy: DISABLED (direct internet or not configured) +======================================== +=== RLJobRunner: ablation-pymethods2test-seqmean-arm0 === +[wandb_utils] Fixing permissions on: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/wandb +[wandb_utils] WandB directory ready: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/wandb + HF_TOKEN=****pDbg + HF_HUB_CACHE=/e/data1/datasets/playground/ot-baf/hf_hub + SUPABASE_URL=https://rpzmyuapoqilpghynmza.s... (direct Supabase config) +Environment configured: + TENSOR_PARALLEL_SIZE=1 + NUM_INFERENCE_ENGINES=56 + POLICY_NUM_NODES=14 + WANDB_DIR=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/wandb +Starting Ray cluster with 14 nodes, 4 GPUs/node +Cleaning up existing Ray instances... +=== Starting Ray Cluster === + Nodes: 14 + GPUs per node: 4 + CPUs per node: 288 + Head node: jpbo-001-35 (10.128.16.35) + Ray port: 6379 +============================ + Starting Ray head on jpbo-001-35 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_head_jpbo-001-35.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.35 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-35 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --head --node-ip-address=10.128.16.35 --port=6379 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray head on jpbo-001-35 + Starting Ray worker on jpbo-001-36 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-36.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.36 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-36 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.36 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 1 on jpbo-001-36 + Starting Ray worker on jpbo-001-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.37 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.37 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 2 on jpbo-001-37 + Starting Ray worker on jpbo-001-38 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-38.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.38 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-38 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.38 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 3 on jpbo-001-38 + Starting Ray worker on jpbo-001-41 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-41.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.41 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-41 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.41 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 4 on jpbo-001-41 + Starting Ray worker on jpbo-001-42 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-42.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.42 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-42 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.42 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 5 on jpbo-001-42 + Starting Ray worker on jpbo-001-43 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-43.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.43 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-43 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.43 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 6 on jpbo-001-43 + Starting Ray worker on jpbo-001-44 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-44.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.44 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-44 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.44 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 7 on jpbo-001-44 + Starting Ray worker on jpbo-001-45 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-45.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.45 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-45 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.45 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 8 on jpbo-001-45 + Starting Ray worker on jpbo-001-46 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-46.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.46 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-46 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.46 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 9 on jpbo-001-46 + Starting Ray worker on jpbo-001-47 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-47.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.47 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-47 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.47 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 10 on jpbo-001-47 + Starting Ray worker on jpbo-001-48 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-48.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.48 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-48 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.48 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 11 on jpbo-001-48 + Starting Ray worker on jpbo-003-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-003-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.133 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-003-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.133 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 12 on jpbo-003-37 + Starting Ray worker on jpbo-003-39 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-003-39.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.135 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-003-39 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.135 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 13 on jpbo-003-39 + Waiting for cluster (56 GPUs, 14 nodes)... +Connecting to Ray at 10.128.16.35:6379 (expecting 14 nodes, 56.0 GPUs) +Ray connection established, polling for resources... +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'GPU': 36.0, 'memory': 6664106213376.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'GPU': 36.0, 'memory': 6664106213376.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'GPU': 36.0, 'memory': 6664106213376.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'GPU': 36.0, 'memory': 6664106213376.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=9/14 GPUs=36.0/56.0 resources={'node:10.128.16.41': 1.0, 'memory': 6664106213376.0, 'GPU': 36.0, 'accelerator_type:GH200': 9.0, 'object_store_memory': 386547056640.0, 'CPU': 2592.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=10/14 GPUs=40.0/56.0 resources={'node:10.128.16.41': 1.0, 'GPU': 40.0, 'memory': 7408839753728.0, 'accelerator_type:GH200': 10.0, 'object_store_memory': 429496729600.0, 'CPU': 2880.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=11/14 GPUs=44.0/56.0 resources={'GPU': 44.0, 'memory': 8175128150016.0, 'accelerator_type:GH200': 11.0, 'object_store_memory': 472446402560.0, 'node:10.128.16.135': 1.0, 'CPU': 3168.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'GPU': 48.0, 'memory': 8928165036032.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=12/14 GPUs=48.0/56.0 resources={'memory': 8928165036032.0, 'GPU': 48.0, 'accelerator_type:GH200': 12.0, 'object_store_memory': 515396075520.0, 'node:10.128.16.135': 1.0, 'CPU': 3456.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0} +[Ray wait] nodes=13/14 GPUs=52.0/56.0 resources={'GPU': 52.0, 'memory': 9646728871936.0, 'accelerator_type:GH200': 13.0, 'object_store_memory': 558345748480.0, 'node:10.128.16.135': 1.0, 'CPU': 3744.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0, 'node:10.128.16.46': 1.0} +[Ray wait] nodes=13/14 GPUs=52.0/56.0 resources={'GPU': 52.0, 'memory': 9646728871936.0, 'accelerator_type:GH200': 13.0, 'object_store_memory': 558345748480.0, 'node:10.128.16.135': 1.0, 'CPU': 3744.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.37': 1.0, 'node:10.128.16.35': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.133': 1.0, 'node:10.128.16.46': 1.0} + srun stderr: srun: error: _find_node_record: lookup failure for node "jpbo-123-29" +srun: error: _find_node_record: lookup failure for node "jpbo-123-30" +srun: error: _find_node_record: lookup failure for node "jpbo-123-31" +srun: error: _find_node_record: lookup failure for node "jpbo-123-32" +srun: error: _find_node_record: lookup failure for node "jpbo-123-33" +srun: error: _find_node_record: lookup failure for node "jpbo-123-34" +srun: error: _find_node_record: lookup failure for node "jpbo-123-35" +srun: error: _find_node_record: lookup failure for node "jpbo-123-36" +srun: error: _find_node_record: lookup failure for node "jpbo-123-37" +srun: error: _find_node_record: lookup failure for node "jpbo-123-38" +srun: error: _find_node_record: lookup failure for node "jpbo-123-39" +srun: error: _find_node_record: lookup failure for node "jpbo-123-40" +srun: error: _find_node_record: lookup failure for node "jpbo-123-41" +srun: error: _find_node_record: lookup failure for node "jpbo-123-42" +srun: error: _find_node_record: lookup failure for node "jpbo-123-43" +srun: error: _find_node_record: lookup failure for node "jpbo-123-44" +srun: error: _find_node_record: lookup failure for node "jpbo-123-45" +srun: error: _find_node_record: lookup failure for node "jpbo-123-46" +srun: error: _find_node_record: lookup failure for node "jpbo-123-47" +srun: error: _find_node_record: lookup failure for node "jpbo-123-48" +srun: error: _find_node_record: lookup failure for node "jpbo-124-01" +srun: error: _find_node_record: lookup failure for node "jpbo-124-02" +srun: error: _find_node_record: lookup failure for node "jpbo-124-03" +srun: error: _find_node_record: lookup failure for node "jpbo-124-04" +srun: error: _find_node_record: lookup failure for node "jpbo-124-05" +srun: error: _find_node_record: lookup failure for node "jpbo-124-06" +srun: error: _find_node_record: lookup failure for node "jpbo-124-07" +srun: error: _find_node_record: lookup failure for node "jpbo-124-08" +srun: error: _find_node_record: lookup failure for node "jpbo-124-09" +srun: error: _find_node_record: lookup failure for node "jpbo-124-10" +srun: error: _find_node_record: lookup failure for node "jpbo-124-11" +srun: error: _find_node_record: lookup failure for node "jpbo-124-12" +srun: error: _find_node_record: lookup failure for node "jpbo-124-13" +srun: error: _find_node_record: lookup failure for node "jpbo-124-14" +srun: error: _find_node_record: lookup failure for node "jpbo-124-15" +srun: error: _find_node_record: lookup failure for node "jpbo-124-16" +srun: error: _find_node_record: lookup failure for node "jpbo-124-17" +srun: error: _find_node_record: lookup failure for node "jpbo-124-18" +srun: error: _find_node_record: lookup failure for node "jpbo-124-19" +srun: error: _find_node_record: lookup failure for node "jpbo-124-20" +srun: error: _find_node_record: lookup failure for node "jpbo-124-21" +srun: error: _find_node_record: lookup failure for node "jpbo-124-22" +srun: error: _find_node_record: lookup failure for node "jpbo-124-23" +srun: error: _find_node_record: lookup failure for node "jpbo-124-24" +srun: error: _find_node_record: lookup failure for node "jpbo-124-25" +srun: error: _find_node_record: lookup failure for node "jpbo-124-26" +srun: error: _find_node_record: lookup failure for node "jpbo-124-27" +srun: error: _find_node_record: lookup failure for node "jpbo-124-28" +srun: error: _find_node_record: lookup failure for node "jpbo-124-29" +srun: error: _find_node_record: lookup failure for node "jpbo-124-30" +srun: error: _find_node_record: lookup failure for node "jpbo-124-31" +srun: error: _find_node_record: lookup failure for node "jpbo-124-32" +srun: error: _find_node_record: lookup failure for node "jpbo-124-33" +srun: error: _find_node_record: lookup failure for node "jpbo-124-34" +srun: error: _find_node_record: lookup failure for node "jpbo-124-35" +srun: error: _find_node_record: lookup failure for node "jpbo-124-36" +srun: error: _find_node_record: lookup failure for node "jpbo-124-37" +srun: error: _find_node_record: lookup failure for node "jpbo-124-38" +srun: error: _find_node_record: lookup failure for node "jpbo-124-39" +srun: error: _find_node_record: lookup failure for node "jpbo-124-40" +srun: error: _find_node_record: lookup failure for node "jpbo-124-41" +srun: error: _find_node_record: lookup failure for node "jpbo-124-42" +srun: error: _find_node_record: lookup failure for node "jpbo-124-43" +srun: error: _find_node_record: lookup failure for node "jpbo-124-44" +srun: error: _find_node_record: lookup failure for node "jpbo-124-45" +srun: error: _find_node_record: lookup failure for node "jpbo-124-46" +srun: error: _find_node_record: lookup failure for node "jpbo-124-47" +srun: error: _find_node_record: lookup failure for node "jpbo-124-48" +srun: error: _find_node_record: lookup failure for node "jpbo-125-01" +srun: error: _find_node_record: lookup failure for node "jpbo-125-02" +srun: error: _find_node_record: lookup failure for node "jpbo-125-03" +srun: error: _find_node_record: lookup failure for node "jpbo-125-04" +srun: error: _find_node_record: lookup failure for node "jpbo-125-05" +srun: error: _find_node_record: lookup failure for node "jpbo-125-06" +srun: error: _find_node_record: lookup failure for node "jpbo-125-07" +srun: error: _find_node_record: lookup failure for node "jpbo-125-08" +srun: error: _find_node_record: lookup failure for node "jpbo-125-09" +srun: error: _find_node_record: lookup failure for node "jpbo-125-10" +srun: error: _find_node_record: lookup failure for node "jpbo-125-11" +srun: error: _find_node_record: lookup failure for node "jpbo-125-12" +srun: error: _find_node_record: lookup failure for node "jpbo-125-13" +srun: error: _find_node_record: lookup failure for node "jpbo-125-14" +srun: error: _find_node_record: lookup failure for node "jpbo-125-15" +srun: error: _find_node_record: lookup failure for node "jpbo-125-16" +srun: error: _find_node_record: lookup failure for node "jpbo-125-17" +srun: error: _find_node_record: lookup failure for node "jpbo-125-18" +srun: error: _find_node_record: lookup failure for node "jpbo-125-19" +srun: error: _find_node_record: lookup failure for node "jpbo-125-20" +srun: error: _find_node_record: lookup failure for node "jpbo-125-21" +srun: error: _find_node_record: lookup failure for node "jpbo-125-22" +srun: error: _find_node_record: lookup failure for node "jpbo-125-23" +srun: error: _find_node_record: lookup failure for node "jpbo-125-24" +srun: error: _find_node_record: lookup failure for node "jpbo-125-25" +srun: error: _find_node_record: lookup failure for node "jpbo-125-26" +srun: error: _find_node_record: lookup failure for node "jpbo-125-27" +srun: error: _find_node_record: lookup failure for node "jpbo-125-28" +srun: error: _find_node_record: lookup failure for node "jpbo-125-29" +srun: error: _find_node_record: lookup failure for node "jpbo-125-30" +srun: error: _find_node_record: lookup failure for node "jpbo-125-31" +srun: error: _find_node_record: lookup failure for node "jpbo-125-32" +srun: error: _find_node_record: lookup failure for node "jpbo-125-33" +srun: error: _find_node_record: lookup failure for node "jpbo-125-34" +srun: error: _find_node_record: lookup failure for node "jpbo-125-35" +srun: error: _find_node_record: lookup failure for node "jpbo-125-36" +srun: error: _find_node_record: lookup failure for node "jpbo-125-37" +srun: error: _find_node_record: lookup failure for node "jpbo-125-38" +srun: error: _find_node_record: lookup failure for node "jpbo-125-39" +srun: error: _find_node_record: lookup failure for node "jpbo-125-40" +srun: error: _find_node_record: lookup failure for node "jpbo-125-41" +srun: error: _find_node_record: lookup failure for node "jpbo-125-42" +srun: error: _find_node_record: lookup failure for node "jpbo-125-43" +srun: error: _find_node_record: lookup failure for node "jpbo-125-44" +srun: error: _find_node_record: lookup failure for node "jpbo-125-45" +srun: error: _find_node_record: lookup failure for node "jpbo-125-46" +srun: error: _find_node_record: lookup failure for node "jpbo-125-47" +srun: error: _find_node_record: lookup failure for node "jpbo-125-48" +srun: warning: Invalid hostnames in switch configuration: jpbo-123-[29-48],jpbo-124-[01-48],jpbo-125-[01-48] +2026-06-08 13:34:49,641 INFO worker.py:1821 -- Connecting to existing Ray cluster at address: 10.128.16.35:6379... +2026-06-08 13:34:49,647 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +2026-06-08 13:34:49,649 INFO worker.py:2007 -- Connected to Ray cluster. +/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0 + warnings.warn( +(pid=1699456) [2026-06-08 13:35:34,739 E 1699456 1700253] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1699477) [2026-06-08 13:35:42,369 E 1699477 1701156] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 18x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) +[2026-06-08 13:35:45,201 E 1699412 1699453] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.47) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1699674) [2026-06-08 13:35:45,144 E 1699674 1715593] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 269x across cluster] +(raylet, ip=10.128.16.133) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 5x across cluster] +(raylet, ip=10.128.16.47) [2026-06-08 13:37:33,645 E 1598284 1598284] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.135) [2026-06-08 13:37:44,343 E 3199851 3199851] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.133) [2026-06-08 13:37:47,415 E 2150584 2150584] (raylet) main.cc:1032: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.16.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +Traceback (most recent call last): + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/scripts/ray/wait_for_cluster.py", line 116, in + main() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/scripts/ray/wait_for_cluster.py", line 106, in main + wait_for_cluster( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/scripts/ray/wait_for_cluster.py", line 94, in wait_for_cluster + raise TimeoutError( +TimeoutError: Ray cluster did not reach desired resources within 600 seconds (nodes=13, gpus=52.0) +srun: error: jpbo-001-35: task 0: Exited with exit code 1 + +RL job failed: Ray cluster failed to start within 600s (last error: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-35', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python scripts/ray/wait_for_cluster.py --address 10.128.16.35:6379 --expected-gpus 56 --expected-nodes 14 --timeout 600 --poll-interval 10']' returned non-zero exit status 1.) +Traceback (most recent call last): + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 651, in _wait_for_cluster + result = subprocess.run( + ^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/subprocess.py", line 571, in run + raise CalledProcessError(retcode, process.args, +subprocess.CalledProcessError: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-35', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python scripts/ray/wait_for_cluster.py --address 10.128.16.35:6379 --expected-gpus 56 --expected-nodes 14 --timeout 600 --poll-interval 10']' returned non-zero exit status 1. + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/rl_launch_utils.py", line 1291, in run + training_exit_code = self._run_with_ray() + ^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/rl_launch_utils.py", line 1538, in _run_with_ray + with RayCluster.from_slurm(ray_cfg) as ray_cluster: + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 888, in __enter__ + self.start() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 377, in start + self._wait_for_cluster() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/hpc/ray_utils.py", line 679, in _wait_for_cluster + raise RuntimeError( +RuntimeError: Ray cluster failed to start within 600s (last error: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-35', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python scripts/ray/wait_for_cluster.py --address 10.128.16.35:6379 --expected-gpus 56 --expected-nodes 14 --timeout 600 --poll-interval 10']' returned non-zero exit status 1.) +[RLJobRunner] Crash detected (exit!=0) — preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/ray_logs BEFORE trace upload (so a wall-clock kill can't lose crash evidence)... + Collecting Ray logs from worker jpbo-001-36... + Collecting Ray logs from worker jpbo-001-37... + Collecting Ray logs from worker jpbo-001-38... + Collecting Ray logs from worker jpbo-001-41... + Collecting Ray logs from worker jpbo-001-42... + Collecting Ray logs from worker jpbo-001-43... + Collecting Ray logs from worker jpbo-001-44... + Collecting Ray logs from worker jpbo-001-45... + Collecting Ray logs from worker jpbo-001-46... + Collecting Ray logs from worker jpbo-001-47... + Collecting Ray logs from worker jpbo-001-48... + Collecting Ray logs from worker jpbo-003-37... + Collecting Ray logs from worker jpbo-003-39... +[RLJobRunner] Crash-time Ray log preservation complete. +[RLJobRunner] No trace_jobs directory found at /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/trace_jobs, skipping upload. +Preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ray_logs/ + Collecting Ray logs from worker jpbo-001-36... + Collecting Ray logs from worker jpbo-001-37... + Collecting Ray logs from worker jpbo-001-38... + Collecting Ray logs from worker jpbo-001-41... + Collecting Ray logs from worker jpbo-001-42... + Collecting Ray logs from worker jpbo-001-43... + Collecting Ray logs from worker jpbo-001-44... + Collecting Ray logs from worker jpbo-001-45... + Collecting Ray logs from worker jpbo-001-46... + Collecting Ray logs from worker jpbo-001-47... + Collecting Ray logs from worker jpbo-001-48... + Collecting Ray logs from worker jpbo-003-37... + Collecting Ray logs from worker jpbo-003-39... +Ray log preservation complete diff --git a/training_logs/ablation-pymethods2test-seqmean-arm0_661734.out b/training_logs/ablation-pymethods2test-seqmean-arm0_661734.out new file mode 100644 index 0000000..1466bba --- /dev/null +++ b/training_logs/ablation-pymethods2test-seqmean-arm0_661734.out @@ -0,0 +1,37567 @@ + +The following have been reloaded with a version change: + 1) GCCcore/.14.3.0 => GCCcore/14.3.0 + + +Lmod is automatically replacing "GCC/14.3.0" with +"nvidia-compilers/25.9-CUDA-13". + +Deactivating conda environment: /e/scratch/jureap59/feuer1/miniforge3/envs/otagent +Activating RL environment: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl +Python executable: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python path check: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +[ray] RAY_TMPDIR=/tmp/ray/ray_661734 +[triton_cache] Triton cache: /tmp/triton_cache_feuer1_661734 +[triton_cache] TorchInductor cache: /tmp/torchinductor_cache_feuer1_661734 +[proxy] ✓ Found proxychains binary at /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 +[proxy] Setting up SSH tunnel to jpbl-s01-01 +[proxy] SSH key: /e/home/jusers/feuer1/jupiter/.ssh/authorized_keys/id_ed25519_jsc +[proxy] Tunnel port: 7003 +[proxy] Node IP: 10.128.16.35 (workers will connect here) +[proxy] ✓ SSH tunnel started successfully +[proxy] ✓ Generated proxychains config at /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_661734.conf +[proxy] - Internal traffic (10.x.x.x, 172.x.x.x, 169.254.x.x) → DIRECT +[proxy] - External traffic (internet) → PROXY via tunnel +[proxy] ✓ Daytona timeout settings configured +[proxy] Testing proxy connectivity... +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_661734.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +[proxy] ✓ Proxy connectivity test passed (huggingface.co reachable via wrapped binary) +[proxy] ⚠ Tunnel not accessible at 10.128.16.35:7003 (workers may fail) +[proxy] ✓ Proxy setup complete (using wrapped binary for Ray workers) +[container_runtime] Using cloud backend: daytona (no local container setup) +=== Universal RL Training Runner === +Config: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/configs/ablation-pymethods2test-seqmean-arm0_rl_config.json +Working directory: /e/scratch/jureap59/feuer1/OpenThoughts-Agent +Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python +Python version: Python 3.12.12 +UV_USE_IO_URING: 0 +Proxy: DISABLED (direct internet or not configured) +======================================== +=== RLJobRunner: ablation-pymethods2test-seqmean-arm0 === +[wandb_utils] Fixing permissions on: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/wandb +[wandb_utils] WandB directory ready: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/wandb + HF_TOKEN=****pDbg + HF_HUB_CACHE=/e/data1/datasets/playground/ot-baf/hf_hub + SUPABASE_URL=https://rpzmyuapoqilpghynmza.s... (direct Supabase config) +Environment configured: + TENSOR_PARALLEL_SIZE=1 + NUM_INFERENCE_ENGINES=56 + POLICY_NUM_NODES=14 + WANDB_DIR=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/wandb +Starting Ray cluster with 14 nodes, 4 GPUs/node +Cleaning up existing Ray instances... +=== Starting Ray Cluster === + Nodes: 14 + GPUs per node: 4 + CPUs per node: 288 + Head node: jpbo-001-35 (10.128.16.35) + Ray port: 6379 +============================ + Starting Ray head on jpbo-001-35 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_head_jpbo-001-35.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.35 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-35 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --head --node-ip-address=10.128.16.35 --port=6379 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray head on jpbo-001-35 + Starting Ray worker on jpbo-001-36 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-36.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.36 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-36 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.36 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 1 on jpbo-001-36 + Starting Ray worker on jpbo-001-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.37 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.37 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 2 on jpbo-001-37 + Starting Ray worker on jpbo-001-38 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-38.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.38 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-38 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.38 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 3 on jpbo-001-38 + Starting Ray worker on jpbo-001-41 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-41.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.41 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-41 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.41 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 4 on jpbo-001-41 + Starting Ray worker on jpbo-001-42 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-42.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.42 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-42 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.42 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 5 on jpbo-001-42 + Starting Ray worker on jpbo-001-43 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-43.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.43 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-43 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.43 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 6 on jpbo-001-43 + Starting Ray worker on jpbo-001-44 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-44.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.44 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-44 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.44 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 7 on jpbo-001-44 + Starting Ray worker on jpbo-001-45 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-45.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.45 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-45 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.45 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 8 on jpbo-001-45 + Starting Ray worker on jpbo-001-46 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-46.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.46 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-46 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.46 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 9 on jpbo-001-46 + Starting Ray worker on jpbo-001-47 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-47.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.47 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-47 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.47 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 10 on jpbo-001-47 + Starting Ray worker on jpbo-001-48 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-001-48.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.48 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-001-48 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.48 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 11 on jpbo-001-48 + Starting Ray worker on jpbo-003-37 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-003-37.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.133 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-003-37 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.133 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 12 on jpbo-003-37 + Starting Ray worker on jpbo-003-39 (logging to /e/data1/datasets/playground/ot-baf/experiments/_ray_logs/ray_worker_jpbo-003-39.log)... + Command: srun --export=ALL,VLLM_HOST_IP=10.128.16.135 --nodes=1 --ntasks=1 --gres=gpu:4 --gpu-bind=none --overlap --cpu-bind=none -w jpbo-003-39 bash -c /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f "$PROXYCHAINS_CONF_FILE" ray start --address=10.128.16.35:6379 --node-ip-address=10.128.16.135 --num-gpus=4 --num-cpus=288 --block --object-store-memory=42949672960 + Started Ray worker 13 on jpbo-003-39 + Waiting for cluster (56 GPUs, 14 nodes)... +Connecting to Ray at 10.128.16.35:6379 (expecting 14 nodes, 56.0 GPUs) +Ray connection established, polling for resources... +[Ray wait] nodes=14/14 GPUs=56.0/56.0 resources={'GPU': 56.0, 'CPU': 4032.0, 'node:10.128.16.48': 1.0, 'memory': 10365118316544.0, 'object_store_memory': 601295421440.0, 'accelerator_type:GH200': 14.0, 'node:10.128.16.133': 1.0, 'node:10.128.16.36': 1.0, 'node:10.128.16.135': 1.0, 'node:10.128.16.42': 1.0, 'node:10.128.16.45': 1.0, 'node:10.128.16.38': 1.0, 'node:10.128.16.44': 1.0, 'node:10.128.16.37': 1.0, 'node:__internal_head__': 1.0, 'node:10.128.16.35': 1.0, 'node:10.128.16.41': 1.0, 'node:10.128.16.47': 1.0, 'node:10.128.16.43': 1.0, 'node:10.128.16.46': 1.0} +✓ Ray cluster ready +=== Ray Cluster Ready === + Address: 10.128.16.35:6379 + Total GPUs: 56 +========================= +Ray cluster ready at 10.128.16.35:6379 +Total GPUs available: 56 +[RLJobRunner] Pinggy check: url=False, token=False, needs_tunnel=False (agent=terminus-2, env=daytona) +[RLJobRunner] No Pinggy tunnel needed, using local vLLM + +Running SkyRL: + Python: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python + Entrypoint: examples.terminal_bench.entrypoints.main_tbench + Args: 123 Hydra arguments + Working dir: /e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train +Using proxychains binary: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 + +Executing command with srun: /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/bin/proxychains4 -f $PROXYCHAINS_CONF_FILE /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/bin/python -m examples.terminal_bench.entrypoints.main_tbench +terminal_bench_config=terminal_bench trainer.strategy=fsdp2 trainer.algorithm.advantage_estimator=rloo_n trainer.algorithm.use_kl_loss=false trainer.algorithm.kl_loss_coef=0.0 trainer.algorithm.eps_clip_low=0.2 trainer.algorithm.eps_clip_high=0.05 trainer.algorithm.loss_reduction=sequence_mean trainer.epochs=2 trainer.max_steps=80 trainer.update_epochs_per_batch=1 trainer.train_batch_size=64 trainer.policy_mini_batch_size=64 trainer.eval_batch_size=64 trainer.micro_forward_batch_size_per_gpu=4 trainer.micro_train_batch_size_per_gpu=1 trainer.max_prompt_length=999999 trainer.eval_interval=999999 trainer.eval_before_train=false trainer.ckpt_interval=2 trainer.resume_mode=latest trainer.hf_save_interval=5 ++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0 ++trainer.hf_hub_private=false ++trainer.hf_hub_revision=main ++trainer.enable_db_registration=false trainer.project_name=OpenThoughts-Agent trainer.log_level=INFO trainer.tracker_commit_each_step=true trainer.logger=console trainer.run_name=ablation-pymethods2test-seqmean-arm0 trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/checkpoints trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/exports trainer.policy.optimizer_config.lr=8e-6 trainer.policy.optimizer_config.weight_decay=0.0 trainer.policy.optimizer_config.adam_betas=[0.9,0.999] trainer.policy.optimizer_config.max_grad_norm=0.9 trainer.policy.fsdp_config.cpu_offload=false trainer.policy.fsdp_config.reshard_after_forward=true trainer.policy.fsdp_config.fsdp_size=4 trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 trainer.ref.fsdp_config.cpu_offload=false trainer.ref.fsdp_config.reshard_after_forward=true trainer.ref.fsdp_config.fsdp_size=4 trainer.placement.colocate_all=false trainer.placement.policy_num_nodes=2 trainer.placement.ref_num_nodes=2 trainer.placement.policy_num_gpus_per_node=4 trainer.placement.ref_num_gpus_per_node=4 trainer.fully_async.max_staleness_steps=16 trainer.fully_async.num_parallel_generation_workers=338 generator.backend=vllm generator.timeout_multiplier=1.0 generator.model_dtype=bfloat16 generator.inference_engine_tensor_parallel_size=1 generator.num_inference_engines=48 generator.n_samples_per_prompt=8 generator.eval_n_samples_per_prompt=8 generator.gpu_memory_utilization=0.75 generator.max_num_seqs=24 generator.max_num_batched_tokens=65536 generator.enable_prefix_caching=true generator.enable_chunked_prefill=true generator.run_engines_locally=true generator.weight_sync_backend=nccl generator.async_engine=true generator.batched=false generator.enable_http_endpoint=true generator.enable_ray_prometheus_stats=false generator.vllm_stats_interval=1 generator.append_eos_token_after_stop_str_in_multi_turn=true generator.max_turns=999999 generator.sampling_params.max_generate_length=4096 generator.sampling_params.temperature=0.7 generator.sampling_params.top_p=0.95 generator.sampling_params.top_k=20 ++generator.engine_init_kwargs.max_model_len=32768 ++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2 ++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"] data.val_data=[] +terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/trace_jobs +terminal_bench_config.harbor.name=terminus-2 +terminal_bench_config.harbor.max_episodes=999999 +terminal_bench_config.harbor.enable_summarize=false +terminal_bench_config.harbor.store_all_messages=true +terminal_bench_config.harbor.trajectory_config.raw_content=true +terminal_bench_config.harbor.enable_episode_logging=false +terminal_bench_config.harbor.record_terminal_session=false +terminal_bench_config.harbor.enable_pane_logging=false +terminal_bench_config.harbor.strict_json_parser=true +terminal_bench_config.harbor.interleaved_thinking=true +terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true +terminal_bench_config.harbor.override_timeout_sec=900 +terminal_bench_config.harbor.override_cpus=1 +terminal_bench_config.harbor.override_memory_mb=2048 +terminal_bench_config.harbor.override_storage_mb=2048 +terminal_bench_config.harbor.auto_snapshot=true +terminal_bench_config.harbor.verifier_override_timeout_sec=120 +terminal_bench_config.harbor.max_retries=3 +terminal_bench_config.harbor.min_wait_sec=60.0 +terminal_bench_config.harbor.max_wait_sec=600.0 +terminal_bench_config.harbor.wait_multiplier=2.0 +terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"] +terminal_bench_config.harbor.n_concurrent_trials=675 +terminal_bench_config.harbor.log_level=INFO +terminal_bench_config.harbor.enable_reward_shaping=false +terminal_bench_config.harbor.enable_error_classification=true +terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"] +terminal_bench_config.harbor.default_error_treatment=zero +terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"] +terminal_bench_config.harbor.zero_exceptions=[] +terminal_bench_config.model_info.max_input_tokens=32000 +terminal_bench_config.model_info.max_output_tokens=4096 +terminal_bench_config.archiving.enabled=false +terminal_bench_config.trace_upload.enabled=true +terminal_bench_config.trace_upload.repo_org=DCAgent +terminal_bench_config.trace_upload.episodes=last +terminal_bench_config.trace_upload.dataset_type=SFT +terminal_bench_config.trace_upload.cleanup=true trainer.enable_db_registration=false trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints trainer.resume_mode=latest +[proxychains] config file found: /e/home/jusers/feuer1/jupiter/.proxychains/proxychains_661734.conf +[proxychains] preloading /e/scratch/jureap59/feuer1/proxychains-ng-aarch64/lib/libproxychains4.so +[proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +2026-06-08 14:08:40.877 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:765 - Exporting wandb api key to ray runtime env +2026-06-08 14:08:40.887 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:784 - Exporting RAY_ADDRESS to ray runtime env +2026-06-08 14:08:40.887 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:809 - Exporting `NCCL_SOCKET_IFNAME` to ray runtime env: ib0 +2026-06-08 14:08:40.887 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:809 - Exporting `NCCL_SOCKET_FAMILY` to ray runtime env: AF_INET +2026-06-08 14:08:40.888 | INFO | skyrl_train.utils.utils:prepare_runtime_environment:809 - Exporting `NCCL_DEBUG` to ray runtime env: WARN +2026-06-08 14:08:40,888 INFO worker.py:1680 -- Using address 10.128.16.35:6379 set in the environment variable RAY_ADDRESS +2026-06-08 14:08:40,918 INFO worker.py:1821 -- Connecting to existing Ray cluster at address: 10.128.16.35:6379... +2026-06-08 14:08:40,925 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +2026-06-08 14:08:40,928 INFO worker.py:2007 -- Connected to Ray cluster. +/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0 + warnings.warn( +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.16.36) 2026-06-08 14:08:42,227 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +2026-06-08 14:08:43.562 | INFO | skyrl_train.utils.ppo_utils:sync_registries:546 - Synced registries to ray actor +(skyrl_entrypoint pid=1755734) 2026-06-08 14:08:53.279 | INFO  | skyrl_train.entrypoints.main_base:_configure_log_level:214 - SkyRL log level set to: INFO +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 17x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) +(raylet) 2026-06-08 14:08:44,913 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 2x across cluster] +(skyrl_entrypoint pid=1755734) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(skyrl_entrypoint pid=1755734) 2026-06-08 14:08:54.706 | INFO  | examples.terminal_bench.dataset:_load_data_files:40 - Loading data from: /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=1755734) 2026-06-08 14:09:00.008 | INFO  | examples.terminal_bench.dataset:_load_data_files:50 - Found 5000 valid task directories out of 5000 total directories +(skyrl_entrypoint pid=1755734) 2026-06-08 14:09:00.008 | INFO  | examples.terminal_bench.dataset:__init__:27 - TerminalBenchTaskDataset initialized with 5000 task paths +(skyrl_entrypoint pid=1755734) 2026-06-08 14:09:00.019 | INFO  | skyrl_train.entrypoints.main_base:_setup_trainer:407 - data: +(skyrl_entrypoint pid=1755734) train_data: +(skyrl_entrypoint pid=1755734) - /e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large +(skyrl_entrypoint pid=1755734) val_data: [] +(skyrl_entrypoint pid=1755734) trainer: +(skyrl_entrypoint pid=1755734) placement: +(skyrl_entrypoint pid=1755734) colocate_all: false +(skyrl_entrypoint pid=1755734) colocate_policy_ref: true +(skyrl_entrypoint pid=1755734) policy_num_nodes: 2 +(skyrl_entrypoint pid=1755734) policy_num_gpus_per_node: 4 +(skyrl_entrypoint pid=1755734) critic_num_nodes: 1 +(skyrl_entrypoint pid=1755734) critic_num_gpus_per_node: 4 +(skyrl_entrypoint pid=1755734) ref_num_nodes: 2 +(skyrl_entrypoint pid=1755734) ref_num_gpus_per_node: 4 +(skyrl_entrypoint pid=1755734) policy_strict_spread_pg: false +(skyrl_entrypoint pid=1755734) policy_per_gpu_bundles: false +(skyrl_entrypoint pid=1755734) policy_force_cvd_mask: false +(skyrl_entrypoint pid=1755734) sequence_parallel_backend: ulysses +(skyrl_entrypoint pid=1755734) strategy: fsdp2 +(skyrl_entrypoint pid=1755734) policy: +(skyrl_entrypoint pid=1755734) model: +(skyrl_entrypoint pid=1755734) path: /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=1755734) lora: +(skyrl_entrypoint pid=1755734) rank: 0 +(skyrl_entrypoint pid=1755734) alpha: 16 +(skyrl_entrypoint pid=1755734) dropout: 0 +(skyrl_entrypoint pid=1755734) lora_sync_path: /tmp/skyrl_lora_sync +(skyrl_entrypoint pid=1755734) target_modules: all-linear +(skyrl_entrypoint pid=1755734) exclude_modules: null +(skyrl_entrypoint pid=1755734) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=1755734) optimizer_config: +(skyrl_entrypoint pid=1755734) optimizer: AdamW +(skyrl_entrypoint pid=1755734) lr: 8.0e-06 +(skyrl_entrypoint pid=1755734) adam_betas: +(skyrl_entrypoint pid=1755734) - 0.9 +(skyrl_entrypoint pid=1755734) - 0.999 +(skyrl_entrypoint pid=1755734) weight_decay: 0.0 +(skyrl_entrypoint pid=1755734) max_grad_norm: 0.9 +(skyrl_entrypoint pid=1755734) offload_after_step: true +(skyrl_entrypoint pid=1755734) num_warmup_steps: 0 +(skyrl_entrypoint pid=1755734) scheduler: constant_with_warmup +(skyrl_entrypoint pid=1755734) optimizer_kwargs: {} +(skyrl_entrypoint pid=1755734) fsdp_config: +(skyrl_entrypoint pid=1755734) cpu_offload: false +(skyrl_entrypoint pid=1755734) reshard_after_forward: true +(skyrl_entrypoint pid=1755734) fsdp_size: 4 +(skyrl_entrypoint pid=1755734) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=1755734) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=1755734) moe_router_replay: false +(skyrl_entrypoint pid=1755734) moe_grouped_gemm: false +(skyrl_entrypoint pid=1755734) ep_comm_backend: torch +(skyrl_entrypoint pid=1755734) deepep_num_sms: 20 +(skyrl_entrypoint pid=1755734) deepep_token_chunk_size: null +(skyrl_entrypoint pid=1755734) sequence_parallel_size: 1 +(skyrl_entrypoint pid=1755734) use_torch_compile: false +(skyrl_entrypoint pid=1755734) record_memory: false +(skyrl_entrypoint pid=1755734) megatron_config: +(skyrl_entrypoint pid=1755734) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) context_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_tensor_parallel_size: null +(skyrl_entrypoint pid=1755734) ddp_config: +(skyrl_entrypoint pid=1755734) grad_reduce_in_fp32: true +(skyrl_entrypoint pid=1755734) overlap_grad_reduce: false +(skyrl_entrypoint pid=1755734) overlap_param_gather: false +(skyrl_entrypoint pid=1755734) average_in_collective: true +(skyrl_entrypoint pid=1755734) model_config_kwargs: {} +(skyrl_entrypoint pid=1755734) torch_profiler_config: +(skyrl_entrypoint pid=1755734) enable: false +(skyrl_entrypoint pid=1755734) ranks: [] +(skyrl_entrypoint pid=1755734) save_path: null +(skyrl_entrypoint pid=1755734) optimizer_config_kwargs: +(skyrl_entrypoint pid=1755734) overlap_cpu_optimizer_d2h_h2d: false +(skyrl_entrypoint pid=1755734) use_precision_aware_optimizer: false +(skyrl_entrypoint pid=1755734) optimizer_cpu_offload: false +(skyrl_entrypoint pid=1755734) optimizer_offload_fraction: 0.0 +(skyrl_entrypoint pid=1755734) transformer_config_kwargs: +(skyrl_entrypoint pid=1755734) recompute_granularity: full +(skyrl_entrypoint pid=1755734) recompute_modules: +(skyrl_entrypoint pid=1755734) - core_attn +(skyrl_entrypoint pid=1755734) recompute_method: uniform +(skyrl_entrypoint pid=1755734) recompute_num_layers: 1 +(skyrl_entrypoint pid=1755734) empty_cuda_cache: true +(skyrl_entrypoint pid=1755734) ref: +(skyrl_entrypoint pid=1755734) model: +(skyrl_entrypoint pid=1755734) path: ${trainer.policy.model.path} +(skyrl_entrypoint pid=1755734) sequence_parallel_size: 1 +(skyrl_entrypoint pid=1755734) deepspeed_config: ${deepspeed_config.eval} +(skyrl_entrypoint pid=1755734) fsdp_config: +(skyrl_entrypoint pid=1755734) cpu_offload: false +(skyrl_entrypoint pid=1755734) reshard_after_forward: true +(skyrl_entrypoint pid=1755734) fsdp_size: 4 +(skyrl_entrypoint pid=1755734) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=1755734) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=1755734) moe_router_replay: false +(skyrl_entrypoint pid=1755734) moe_grouped_gemm: false +(skyrl_entrypoint pid=1755734) ep_comm_backend: torch +(skyrl_entrypoint pid=1755734) deepep_num_sms: 20 +(skyrl_entrypoint pid=1755734) deepep_token_chunk_size: null +(skyrl_entrypoint pid=1755734) megatron_config: +(skyrl_entrypoint pid=1755734) tensor_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) pipeline_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) context_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=1755734) model_config_kwargs: {} +(skyrl_entrypoint pid=1755734) transformer_config_kwargs: {} +(skyrl_entrypoint pid=1755734) critic: +(skyrl_entrypoint pid=1755734) model: +(skyrl_entrypoint pid=1755734) path: null +(skyrl_entrypoint pid=1755734) lora: +(skyrl_entrypoint pid=1755734) rank: 0 +(skyrl_entrypoint pid=1755734) alpha: 16 +(skyrl_entrypoint pid=1755734) dropout: 0 +(skyrl_entrypoint pid=1755734) target_modules: all-linear +(skyrl_entrypoint pid=1755734) exclude_modules: null +(skyrl_entrypoint pid=1755734) deepspeed_config: ${deepspeed_config.train} +(skyrl_entrypoint pid=1755734) optimizer_config: +(skyrl_entrypoint pid=1755734) optimizer: AdamW +(skyrl_entrypoint pid=1755734) lr: 5.0e-06 +(skyrl_entrypoint pid=1755734) adam_betas: +(skyrl_entrypoint pid=1755734) - 0.9 +(skyrl_entrypoint pid=1755734) - 0.999 +(skyrl_entrypoint pid=1755734) weight_decay: 0.01 +(skyrl_entrypoint pid=1755734) max_grad_norm: 1.0 +(skyrl_entrypoint pid=1755734) offload_after_step: true +(skyrl_entrypoint pid=1755734) num_warmup_steps: 0 +(skyrl_entrypoint pid=1755734) scheduler: constant_with_warmup +(skyrl_entrypoint pid=1755734) optimizer_kwargs: {} +(skyrl_entrypoint pid=1755734) fsdp_config: +(skyrl_entrypoint pid=1755734) cpu_offload: false +(skyrl_entrypoint pid=1755734) reshard_after_forward: true +(skyrl_entrypoint pid=1755734) fsdp_size: -1 +(skyrl_entrypoint pid=1755734) expert_model_parallel_size: 1 +(skyrl_entrypoint pid=1755734) expert_tensor_parallel_size: 1 +(skyrl_entrypoint pid=1755734) moe_token_dispatcher_type: alltoall +(skyrl_entrypoint pid=1755734) moe_router_replay: false +(skyrl_entrypoint pid=1755734) moe_grouped_gemm: false +(skyrl_entrypoint pid=1755734) ep_comm_backend: torch +(skyrl_entrypoint pid=1755734) deepep_num_sms: 20 +(skyrl_entrypoint pid=1755734) deepep_token_chunk_size: null +(skyrl_entrypoint pid=1755734) sequence_parallel_size: 1 +(skyrl_entrypoint pid=1755734) algorithm: +(skyrl_entrypoint pid=1755734) advantage_estimator: rloo_n +(skyrl_entrypoint pid=1755734) kl_ctrl: +(skyrl_entrypoint pid=1755734) type: fixed +(skyrl_entrypoint pid=1755734) kl_target: 0.1 +(skyrl_entrypoint pid=1755734) horizon: 10000 +(skyrl_entrypoint pid=1755734) kl_estimator_type: k3 +(skyrl_entrypoint pid=1755734) use_kl_estimator_k3: false +(skyrl_entrypoint pid=1755734) use_abs_kl: false +(skyrl_entrypoint pid=1755734) use_kl_in_reward: false +(skyrl_entrypoint pid=1755734) use_kl_loss: false +(skyrl_entrypoint pid=1755734) kl_loss_coef: 0.0 +(skyrl_entrypoint pid=1755734) use_entropy_loss: false +(skyrl_entrypoint pid=1755734) entropy_loss_coef: 0.01 +(skyrl_entrypoint pid=1755734) advantage_batch_normalize: false +(skyrl_entrypoint pid=1755734) value_head_prefix: value_head +(skyrl_entrypoint pid=1755734) policy_loss_type: regular +(skyrl_entrypoint pid=1755734) loss_reduction: sequence_mean +(skyrl_entrypoint pid=1755734) global_loss_denom: null +(skyrl_entrypoint pid=1755734) grpo_norm_by_std: true +(skyrl_entrypoint pid=1755734) rloo_n_min_group_size: 4 +(skyrl_entrypoint pid=1755734) rloo_n_filter_zero_reward_groups: true +(skyrl_entrypoint pid=1755734) lambd: 1.0 +(skyrl_entrypoint pid=1755734) gamma: 1.0 +(skyrl_entrypoint pid=1755734) eps_clip_low: 0.2 +(skyrl_entrypoint pid=1755734) eps_clip_high: 0.05 +(skyrl_entrypoint pid=1755734) clip_ratio_c: 3.0 +(skyrl_entrypoint pid=1755734) tis_imp_ratio_cap: -1.0 +(skyrl_entrypoint pid=1755734) use_tis: false +(skyrl_entrypoint pid=1755734) sapo: +(skyrl_entrypoint pid=1755734) tau_pos: 1.0 +(skyrl_entrypoint pid=1755734) tau_neg: 1.05 +(skyrl_entrypoint pid=1755734) value_clip: 0.2 +(skyrl_entrypoint pid=1755734) dynamic_sampling: +(skyrl_entrypoint pid=1755734) type: null +(skyrl_entrypoint pid=1755734) max_sample_batches: 30 +(skyrl_entrypoint pid=1755734) min_replace_ratio: 0.3 +(skyrl_entrypoint pid=1755734) clip_cov: +(skyrl_entrypoint pid=1755734) clip_ratio: 0.0002 +(skyrl_entrypoint pid=1755734) clip_cov_lb: 1.0 +(skyrl_entrypoint pid=1755734) clip_cov_ub: 5.0 +(skyrl_entrypoint pid=1755734) kl_cov: +(skyrl_entrypoint pid=1755734) kl_cov_frac: 0.2 +(skyrl_entrypoint pid=1755734) ppo_kl_coef: 1.0 +(skyrl_entrypoint pid=1755734) cispo: +(skyrl_entrypoint pid=1755734) cispo_eps_clip_low: 0 +(skyrl_entrypoint pid=1755734) cispo_eps_clip_high: 5 +(skyrl_entrypoint pid=1755734) z_clip: +(skyrl_entrypoint pid=1755734) enabled: false +(skyrl_entrypoint pid=1755734) alpha: 0.97 +(skyrl_entrypoint pid=1755734) z_thresh: 2.5 +(skyrl_entrypoint pid=1755734) warmup_steps: 25 +(skyrl_entrypoint pid=1755734) mode: zscore +(skyrl_entrypoint pid=1755734) clip_option: adaptive_scaling +(skyrl_entrypoint pid=1755734) clip_factor: 1.0 +(skyrl_entrypoint pid=1755734) skip_update_on_spike: false +(skyrl_entrypoint pid=1755734) stale_clip: +(skyrl_entrypoint pid=1755734) enabled: false +(skyrl_entrypoint pid=1755734) alpha: 0.3 +(skyrl_entrypoint pid=1755734) entropy_threshold: 0.15 +(skyrl_entrypoint pid=1755734) entropy_window: 10 +(skyrl_entrypoint pid=1755734) min_lr_scale: 0.1 +(skyrl_entrypoint pid=1755734) max_seq_len: 1004095 +(skyrl_entrypoint pid=1755734) fully_async: +(skyrl_entrypoint pid=1755734) max_staleness_steps: 16 +(skyrl_entrypoint pid=1755734) num_parallel_generation_workers: 338 +(skyrl_entrypoint pid=1755734) gradient_checkpointing: true +(skyrl_entrypoint pid=1755734) gradient_checkpointing_use_reentrant: false +(skyrl_entrypoint pid=1755734) seed: 42 +(skyrl_entrypoint pid=1755734) resume_mode: latest +(skyrl_entrypoint pid=1755734) resume_path: null +(skyrl_entrypoint pid=1755734) ckpt_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints +(skyrl_entrypoint pid=1755734) max_ckpts_to_keep: -1 +(skyrl_entrypoint pid=1755734) ckpt_interval: 2 +(skyrl_entrypoint pid=1755734) hf_save_interval: 5 +(skyrl_entrypoint pid=1755734) hf_upload_mode: latest +(skyrl_entrypoint pid=1755734) export_path: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/exports +(skyrl_entrypoint pid=1755734) bf16: true +(skyrl_entrypoint pid=1755734) epochs: 2 +(skyrl_entrypoint pid=1755734) max_steps: 80 +(skyrl_entrypoint pid=1755734) update_epochs_per_batch: 1 +(skyrl_entrypoint pid=1755734) train_batch_size: 64 +(skyrl_entrypoint pid=1755734) policy_mini_batch_size: 64 +(skyrl_entrypoint pid=1755734) critic_mini_batch_size: 256 +(skyrl_entrypoint pid=1755734) micro_train_batch_size_per_gpu: 1 +(skyrl_entrypoint pid=1755734) micro_forward_batch_size_per_gpu: 4 +(skyrl_entrypoint pid=1755734) update_ref_every_epoch: false +(skyrl_entrypoint pid=1755734) use_sample_packing: true +(skyrl_entrypoint pid=1755734) eval_batch_size: 64 +(skyrl_entrypoint pid=1755734) eval_before_train: false +(skyrl_entrypoint pid=1755734) eval_interval: 999999 +(skyrl_entrypoint pid=1755734) max_prompt_length: 999999 +(skyrl_entrypoint pid=1755734) flash_attn: true +(skyrl_entrypoint pid=1755734) disable_fast_tokenizer: false +(skyrl_entrypoint pid=1755734) target_modules: null +(skyrl_entrypoint pid=1755734) exclude_modules: null +(skyrl_entrypoint pid=1755734) project_name: OpenThoughts-Agent +(skyrl_entrypoint pid=1755734) run_name: ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=1755734) logger: console +(skyrl_entrypoint pid=1755734) tracker_commit_each_step: true +(skyrl_entrypoint pid=1755734) dump_data_batch: false +(skyrl_entrypoint pid=1755734) dump_eval_results: true +(skyrl_entrypoint pid=1755734) log_level: INFO +(skyrl_entrypoint pid=1755734) rope_scaling: null +(skyrl_entrypoint pid=1755734) rope_theta: null +(skyrl_entrypoint pid=1755734) step_wise_training: false +(skyrl_entrypoint pid=1755734) hf_hub_repo_id: laion/ablation-pymethods2test-seqmean-arm0 +(skyrl_entrypoint pid=1755734) hf_hub_private: false +(skyrl_entrypoint pid=1755734) hf_hub_revision: main +(skyrl_entrypoint pid=1755734) enable_db_registration: false +(skyrl_entrypoint pid=1755734) generator: +(skyrl_entrypoint pid=1755734) model_name: ${trainer.policy.model.path} +(skyrl_entrypoint pid=1755734) model_dtype: bfloat16 +(skyrl_entrypoint pid=1755734) timeout_multiplier: 1.0 +(skyrl_entrypoint pid=1755734) run_engines_locally: true +(skyrl_entrypoint pid=1755734) num_inference_engines: 48 +(skyrl_entrypoint pid=1755734) backend: vllm +(skyrl_entrypoint pid=1755734) weight_sync_backend: nccl +(skyrl_entrypoint pid=1755734) fuse_weights: false +(skyrl_entrypoint pid=1755734) weight_transfer_threshold_cuda_ipc_GB: 1.0 +(skyrl_entrypoint pid=1755734) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=1755734) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=1755734) inference_engine_expert_parallel_size: 1 +(skyrl_entrypoint pid=1755734) inference_engine_data_parallel_size: 1 +(skyrl_entrypoint pid=1755734) inference_engine_mp_backend: false +(skyrl_entrypoint pid=1755734) n_samples_per_prompt: 8 +(skyrl_entrypoint pid=1755734) async_engine: true +(skyrl_entrypoint pid=1755734) batched: false +(skyrl_entrypoint pid=1755734) max_input_length: ${trainer.max_prompt_length} +(skyrl_entrypoint pid=1755734) vllm_v1_disable_multiproc: true +(skyrl_entrypoint pid=1755734) enable_prefix_caching: true +(skyrl_entrypoint pid=1755734) enable_chunked_prefill: true +(skyrl_entrypoint pid=1755734) max_num_batched_tokens: 65536 +(skyrl_entrypoint pid=1755734) enforce_eager: true +(skyrl_entrypoint pid=1755734) fully_sharded_loras: false +(skyrl_entrypoint pid=1755734) enable_ray_prometheus_stats: false +(skyrl_entrypoint pid=1755734) vllm_stats_interval: 1 +(skyrl_entrypoint pid=1755734) gpu_memory_utilization: 0.75 +(skyrl_entrypoint pid=1755734) max_num_seqs: 24 +(skyrl_entrypoint pid=1755734) remote_inference_engine_urls: +(skyrl_entrypoint pid=1755734) - 127.0.0.1:8001 +(skyrl_entrypoint pid=1755734) enable_http_endpoint: true +(skyrl_entrypoint pid=1755734) http_endpoint_host: 127.0.0.1 +(skyrl_entrypoint pid=1755734) http_endpoint_port: 8000 +(skyrl_entrypoint pid=1755734) max_turns: 999999 +(skyrl_entrypoint pid=1755734) chat_template: +(skyrl_entrypoint pid=1755734) source: name +(skyrl_entrypoint pid=1755734) name_or_path: null +(skyrl_entrypoint pid=1755734) chat_template_kwargs: {} +(skyrl_entrypoint pid=1755734) engine_init_kwargs: +(skyrl_entrypoint pid=1755734) max_model_len: 32768 +(skyrl_entrypoint pid=1755734) custom_chat_template_chat_completion_path: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=1755734) served_model_name: 0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6 +(skyrl_entrypoint pid=1755734) override_existing_update_group: disable +(skyrl_entrypoint pid=1755734) sampling_params: +(skyrl_entrypoint pid=1755734) max_generate_length: 4096 +(skyrl_entrypoint pid=1755734) repetition_penalty: 1.0 +(skyrl_entrypoint pid=1755734) temperature: 0.7 +(skyrl_entrypoint pid=1755734) top_p: 0.95 +(skyrl_entrypoint pid=1755734) min_p: 0.0 +(skyrl_entrypoint pid=1755734) top_k: 20 +(skyrl_entrypoint pid=1755734) logprobs: null +(skyrl_entrypoint pid=1755734) stop: null +(skyrl_entrypoint pid=1755734) use_conversation_multi_turn: true +(skyrl_entrypoint pid=1755734) append_eos_token_after_stop_str_in_multi_turn: true +(skyrl_entrypoint pid=1755734) eval_sampling_params: +(skyrl_entrypoint pid=1755734) max_generate_length: ${generator.sampling_params.max_generate_length} +(skyrl_entrypoint pid=1755734) repetition_penalty: 1.0 +(skyrl_entrypoint pid=1755734) temperature: 0.0 +(skyrl_entrypoint pid=1755734) top_p: 1.0 +(skyrl_entrypoint pid=1755734) min_p: 0.0 +(skyrl_entrypoint pid=1755734) top_k: -1 +(skyrl_entrypoint pid=1755734) logprobs: null +(skyrl_entrypoint pid=1755734) stop: null +(skyrl_entrypoint pid=1755734) eval_n_samples_per_prompt: 8 +(skyrl_entrypoint pid=1755734) zero_reward_on_non_stop: false +(skyrl_entrypoint pid=1755734) apply_overlong_filtering: false +(skyrl_entrypoint pid=1755734) rope_scaling: ${trainer.rope_scaling} +(skyrl_entrypoint pid=1755734) rope_theta: ${trainer.rope_theta} +(skyrl_entrypoint pid=1755734) teacher: +(skyrl_entrypoint pid=1755734) model_path: null +(skyrl_entrypoint pid=1755734) top_k_logprobs: 256 +(skyrl_entrypoint pid=1755734) num_inference_engines: 1 +(skyrl_entrypoint pid=1755734) inference_engine_tensor_parallel_size: 1 +(skyrl_entrypoint pid=1755734) inference_engine_pipeline_parallel_size: 1 +(skyrl_entrypoint pid=1755734) gpu_memory_utilization: 0.9 +(skyrl_entrypoint pid=1755734) enforce_eager: false +(skyrl_entrypoint pid=1755734) backend: vllm +(skyrl_entrypoint pid=1755734) engine_init_kwargs: {} +(skyrl_entrypoint pid=1755734) environment: +(skyrl_entrypoint pid=1755734) env_class: gsm8k +(skyrl_entrypoint pid=1755734) skyrl_gym: +(skyrl_entrypoint pid=1755734) max_env_workers: 32 +(skyrl_entrypoint pid=1755734) text2sql: +(skyrl_entrypoint pid=1755734) db_path: /home/ray/default/sql_data +(skyrl_entrypoint pid=1755734) llm_as_a_judge: +(skyrl_entrypoint pid=1755734) model: gpt-4o-mini +(skyrl_entrypoint pid=1755734) base_url: null +(skyrl_entrypoint pid=1755734) search: +(skyrl_entrypoint pid=1755734) log_requests: false +(skyrl_entrypoint pid=1755734) search_url: http://127.0.0.1:8000/retrieve +(skyrl_entrypoint pid=1755734) topk: 3 +(skyrl_entrypoint pid=1755734) timeout: 30 +(skyrl_entrypoint pid=1755734) rollout: +(skyrl_entrypoint pid=1755734) fanout: +(skyrl_entrypoint pid=1755734) enabled: true +(skyrl_entrypoint pid=1755734) num_coordinators: 4 +(skyrl_entrypoint pid=1755734) cpus_per_coordinator: 8 +(skyrl_entrypoint pid=1755734) deepspeed_config: +(skyrl_entrypoint pid=1755734) train: +(skyrl_entrypoint pid=1755734) zero_optimization: +(skyrl_entrypoint pid=1755734) stage: 3 +(skyrl_entrypoint pid=1755734) offload_param: +(skyrl_entrypoint pid=1755734) device: none +(skyrl_entrypoint pid=1755734) offload_optimizer: +(skyrl_entrypoint pid=1755734) device: none +(skyrl_entrypoint pid=1755734) pin_memory: true +(skyrl_entrypoint pid=1755734) sub_group_size: auto +(skyrl_entrypoint pid=1755734) reduce_bucket_size: auto +(skyrl_entrypoint pid=1755734) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=1755734) stage3_prefetch_bucket_size: auto +(skyrl_entrypoint pid=1755734) stage3_max_live_parameters: auto +(skyrl_entrypoint pid=1755734) stage3_max_reuse_distance: auto +(skyrl_entrypoint pid=1755734) round_robin_gradients: true +(skyrl_entrypoint pid=1755734) zero_hpz_partition_size: 1 +(skyrl_entrypoint pid=1755734) zero_quantized_weights: false +(skyrl_entrypoint pid=1755734) zero_quantized_gradients: false +(skyrl_entrypoint pid=1755734) torch_autocast: +(skyrl_entrypoint pid=1755734) enabled: true +(skyrl_entrypoint pid=1755734) dtype: bfloat16 +(skyrl_entrypoint pid=1755734) disable_trace_cache: false +(skyrl_entrypoint pid=1755734) data_types: +(skyrl_entrypoint pid=1755734) grad_accum_dtype: fp32 +(skyrl_entrypoint pid=1755734) gradient_clipping: 1.0 +(skyrl_entrypoint pid=1755734) wall_clock_breakdown: false +(skyrl_entrypoint pid=1755734) prescale_gradient: false +(skyrl_entrypoint pid=1755734) eval: +(skyrl_entrypoint pid=1755734) zero_optimization: +(skyrl_entrypoint pid=1755734) stage: 3 +(skyrl_entrypoint pid=1755734) stage3_param_persistence_threshold: auto +(skyrl_entrypoint pid=1755734) offload_param: +(skyrl_entrypoint pid=1755734) device: cpu +(skyrl_entrypoint pid=1755734) pin_memory: true +(skyrl_entrypoint pid=1755734) torch_autocast: +(skyrl_entrypoint pid=1755734) enabled: true +(skyrl_entrypoint pid=1755734) dtype: bfloat16 +(skyrl_entrypoint pid=1755734) gradient_clipping: 1.0 +(skyrl_entrypoint pid=1755734) prescale_gradient: false +(skyrl_entrypoint pid=1755734) wall_clock_breakdown: false +(skyrl_entrypoint pid=1755734) terminal_bench_config: +(skyrl_entrypoint pid=1755734) trials_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/trace_jobs +(skyrl_entrypoint pid=1755734) harbor: +(skyrl_entrypoint pid=1755734) name: terminus-2 +(skyrl_entrypoint pid=1755734) max_episodes: 999999 +(skyrl_entrypoint pid=1755734) enable_summarize: false +(skyrl_entrypoint pid=1755734) store_all_messages: true +(skyrl_entrypoint pid=1755734) trajectory_config: +(skyrl_entrypoint pid=1755734) raw_content: true +(skyrl_entrypoint pid=1755734) enable_episode_logging: false +(skyrl_entrypoint pid=1755734) record_terminal_session: false +(skyrl_entrypoint pid=1755734) enable_pane_logging: false +(skyrl_entrypoint pid=1755734) strict_json_parser: true +(skyrl_entrypoint pid=1755734) interleaved_thinking: true +(skyrl_entrypoint pid=1755734) extra_body: +(skyrl_entrypoint pid=1755734) chat_template_kwargs: +(skyrl_entrypoint pid=1755734) enable_thinking: true +(skyrl_entrypoint pid=1755734) override_timeout_sec: 900 +(skyrl_entrypoint pid=1755734) override_cpus: 1 +(skyrl_entrypoint pid=1755734) override_memory_mb: 2048 +(skyrl_entrypoint pid=1755734) override_storage_mb: 2048 +(skyrl_entrypoint pid=1755734) auto_snapshot: true +(skyrl_entrypoint pid=1755734) verifier_override_timeout_sec: 120 +(skyrl_entrypoint pid=1755734) max_retries: 3 +(skyrl_entrypoint pid=1755734) min_wait_sec: 60.0 +(skyrl_entrypoint pid=1755734) max_wait_sec: 600.0 +(skyrl_entrypoint pid=1755734) wait_multiplier: 2.0 +(skyrl_entrypoint pid=1755734) exclude_exceptions: +(skyrl_entrypoint pid=1755734) - VerifierTimeoutError +(skyrl_entrypoint pid=1755734) - VerifierRuntimeError +(skyrl_entrypoint pid=1755734) - RewardFileNotFoundError +(skyrl_entrypoint pid=1755734) - RewardFileEmptyError +(skyrl_entrypoint pid=1755734) - VerifierOutputParseError +(skyrl_entrypoint pid=1755734) n_concurrent_trials: 675 +(skyrl_entrypoint pid=1755734) log_level: INFO +(skyrl_entrypoint pid=1755734) enable_reward_shaping: false +(skyrl_entrypoint pid=1755734) enable_error_classification: true +(skyrl_entrypoint pid=1755734) mask_exceptions: +(skyrl_entrypoint pid=1755734) - DaytonaError +(skyrl_entrypoint pid=1755734) - EnvironmentStartTimeoutError +(skyrl_entrypoint pid=1755734) - NetworkError +(skyrl_entrypoint pid=1755734) - ConnectionError +(skyrl_entrypoint pid=1755734) - RewardFileNotFoundError +(skyrl_entrypoint pid=1755734) - RewardFileEmptyError +(skyrl_entrypoint pid=1755734) - AgentEnvironmentTimeoutError +(skyrl_entrypoint pid=1755734) - ContextLengthExceededError +(skyrl_entrypoint pid=1755734) default_error_treatment: zero +(skyrl_entrypoint pid=1755734) passthrough_exceptions: +(skyrl_entrypoint pid=1755734) - AgentTimeoutError +(skyrl_entrypoint pid=1755734) zero_exceptions: [] +(skyrl_entrypoint pid=1755734) model_info: +(skyrl_entrypoint pid=1755734) max_input_tokens: 32000 +(skyrl_entrypoint pid=1755734) max_output_tokens: 4096 +(skyrl_entrypoint pid=1755734) archiving: +(skyrl_entrypoint pid=1755734) enabled: false +(skyrl_entrypoint pid=1755734) trace_upload: +(skyrl_entrypoint pid=1755734) enabled: true +(skyrl_entrypoint pid=1755734) repo_org: DCAgent +(skyrl_entrypoint pid=1755734) episodes: last +(skyrl_entrypoint pid=1755734) dataset_type: SFT +(skyrl_entrypoint pid=1755734) cleanup: true +(skyrl_entrypoint pid=1755734)  +(skyrl_entrypoint pid=1755734) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(skyrl_entrypoint pid=1755734) No module named 'vllm._version' +(skyrl_entrypoint pid=1755734) from .version import __version__, __version_tuple__ # isort:skip +[2026-06-08 14:09:11,111 E 1754891 1755708] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(RegistryActor pid=1577721, ip=10.128.16.36) [2026-06-08 14:09:12,245 E 1577721 1577761] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(skyrl_entrypoint pid=1755734) W0608 14:10:10.973000 1755734 envs/rl/lib/python3.12/site-packages/torch/utils/cpp_extension.py:117] No CUDA runtime is found, using CUDA_HOME='/e/software/default/stages/2026/software/CUDA/13' +(skyrl_entrypoint pid=1755734) [2026-06-08 14:09:14,930 E 1755734 1755777] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 2x across cluster] +(raylet) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet) 2026-06-08 14:10:44,222 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(get_all_env_variables pid=1755879) [2026-06-08 14:11:14,241 E 1755879 1755919] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.133) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 29x across cluster] +(raylet, ip=10.128.16.133) 2026-06-08 14:10:46,751 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 4x across cluster] +(pid=2173170, ip=10.128.16.133) [2026-06-08 14:11:16,816 E 2173170 2173274] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(raylet, ip=10.128.16.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 2x across cluster] +(raylet, ip=10.128.16.38) 2026-06-08 14:11:24,180 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=2173170, ip=10.128.16.133) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=2173170, ip=10.128.16.133) No module named 'vllm._version' +(pid=2173170, ip=10.128.16.133) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 10x across cluster] +(raylet, ip=10.128.16.38) 2026-06-08 14:11:24,200 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(raylet, ip=10.128.16.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 2x across cluster] +(raylet, ip=10.128.16.38) 2026-06-08 14:11:50,817 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(get_addr_port pid=1292850, ip=10.128.16.38) [2026-06-08 14:11:54,202 E 1292850 1292902] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1292851, ip=10.128.16.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1292851, ip=10.128.16.38) No module named 'vllm._version' +(pid=1292851, ip=10.128.16.38) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 22x across cluster] +(raylet, ip=10.128.16.43) 2026-06-08 14:11:51,178 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 3x across cluster] +(pid=1292851, ip=10.128.16.38) [2026-06-08 14:11:54,218 E 1292851 1292953] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1292985, ip=10.128.16.38) [2026-06-08 14:12:20,841 E 1292985 1293087] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1292984, ip=10.128.16.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 2x across cluster] +(pid=1292984, ip=10.128.16.38) No module named 'vllm._version' [repeated 2x across cluster] +(pid=1292984, ip=10.128.16.38) from .version import __version__, __version_tuple__ # isort:skip [repeated 2x across cluster] +(raylet, ip=10.128.16.48) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1287233, ip=10.128.16.43) [2026-06-08 14:12:21,245 E 1287233 1287337] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(raylet, ip=10.128.16.43) 2026-06-08 14:13:08,009 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1287233, ip=10.128.16.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1287233, ip=10.128.16.43) No module named 'vllm._version' +(pid=1287233, ip=10.128.16.43) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.48) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 29x across cluster] +(raylet, ip=10.128.16.48) 2026-06-08 14:13:08,180 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 4x across cluster] +(pid=1287376, ip=10.128.16.43) [2026-06-08 14:13:38,031 E 1287376 1287509] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1287377, ip=10.128.16.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1287377, ip=10.128.16.43) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1287377, ip=10.128.16.43) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(raylet, ip=10.128.16.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1650837, ip=10.128.16.48) [2026-06-08 14:13:38,199 E 1650837 1650939] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(raylet, ip=10.128.16.38) 2026-06-08 14:14:43,004 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1293194, ip=10.128.16.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1293194, ip=10.128.16.38) No module named 'vllm._version' +(pid=1293194, ip=10.128.16.38) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.135) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 53x across cluster] +(pid=1651113, ip=10.128.16.48) [2026-06-08 14:15:13,070 E 1651113 1651250] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.135) 2026-06-08 14:14:43,178 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 8x across cluster] +(pid=2173451, ip=10.128.16.133) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=2173451, ip=10.128.16.133) No module named 'vllm._version' [repeated 3x across cluster] +(pid=2173451, ip=10.128.16.133) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(pid=1651114, ip=10.128.16.48) [2026-06-08 14:15:13,064 E 1651114 1651202] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.135) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 3x across cluster] +(pid=1651115, ip=10.128.16.48) [2026-06-08 14:15:13,277 E 1651115 1651279] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 7x across cluster] +(raylet, ip=10.128.16.135) 2026-06-08 14:15:57,963 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=3222484, ip=10.128.16.135) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=3222484, ip=10.128.16.135) No module named 'vllm._version' +(pid=3222484, ip=10.128.16.135) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 27x across cluster] +(raylet, ip=10.128.16.41) 2026-06-08 14:16:00,667 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 4x across cluster] +(pid=3222698, ip=10.128.16.135) [2026-06-08 14:16:27,981 E 3222698 3222813] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=3222697, ip=10.128.16.135) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=3222697, ip=10.128.16.135) No module named 'vllm._version' [repeated 3x across cluster] +(pid=3222697, ip=10.128.16.135) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(raylet, ip=10.128.16.41) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1569537, ip=10.128.16.41) [2026-06-08 14:16:30,731 E 1569537 1569639] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(raylet, ip=10.128.16.41) 2026-06-08 14:17:15,625 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1569537, ip=10.128.16.41) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1569537, ip=10.128.16.41) No module named 'vllm._version' +(pid=1569537, ip=10.128.16.41) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.45) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 29x across cluster] +(raylet, ip=10.128.16.45) 2026-06-08 14:17:15,692 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 4x across cluster] +(pid=1569678, ip=10.128.16.41) [2026-06-08 14:17:45,690 E 1569678 1569817] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1569680, ip=10.128.16.41) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1569680, ip=10.128.16.41) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1569680, ip=10.128.16.41) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1569680, ip=10.128.16.41) [2026-06-08 14:17:45,857 E 1569680 1569845] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 4x across cluster] +(pid=1650837, ip=10.128.16.48) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1650837, ip=10.128.16.48) No module named 'vllm._version' +(pid=1650837, ip=10.128.16.48) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 11x across cluster] +(pid=1651115, ip=10.128.16.48) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1651115, ip=10.128.16.48) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1651115, ip=10.128.16.48) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(raylet, ip=10.128.16.36) 2026-06-08 14:18:45,293 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(get_addr_port pid=1578124, ip=10.128.16.36) [2026-06-08 14:19:15,310 E 1578124 1578201] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.36) 2026-06-08 14:18:45,293 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1578125, ip=10.128.16.36) [2026-06-08 14:19:15,358 E 1578125 1578229] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.36) 2026-06-08 14:19:45,249 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(raylet, ip=10.128.16.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 23x across cluster] +(raylet, ip=10.128.16.36) 2026-06-08 14:19:45,249 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(raylet, ip=10.128.16.37) 2026-06-08 14:19:54,406 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1578125, ip=10.128.16.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1578125, ip=10.128.16.36) No module named 'vllm._version' +(pid=1578125, ip=10.128.16.36) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.37) 2026-06-08 14:19:54,406 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1578320, ip=10.128.16.36) [2026-06-08 14:20:15,266 E 1578320 1578394] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1578319, ip=10.128.16.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 2x across cluster] +(pid=1578319, ip=10.128.16.36) No module named 'vllm._version' [repeated 2x across cluster] +(pid=1578319, ip=10.128.16.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 2x across cluster] +(get_addr_port pid=1580470, ip=10.128.16.37) [2026-06-08 14:20:24,424 E 1580470 1580546] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 2x across cluster] +(raylet, ip=10.128.16.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1580469, ip=10.128.16.37) [2026-06-08 14:20:24,472 E 1580469 1580574] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.37) 2026-06-08 14:21:02,442 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1580613, ip=10.128.16.37) [2026-06-08 14:21:32,459 E 1580613 1580723] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.37) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 17x across cluster] +(raylet, ip=10.128.16.37) 2026-06-08 14:21:02,442 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 2x across cluster] +(pid=1438458, ip=10.128.16.45) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1438458, ip=10.128.16.45) No module named 'vllm._version' +(pid=1438458, ip=10.128.16.45) from .version import __version__, __version_tuple__ # isort:skip +(pid=1580614, ip=10.128.16.37) [2026-06-08 14:21:32,705 E 1580614 1580779] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 2x across cluster] +(raylet, ip=10.128.16.46) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 3x across cluster] +(raylet, ip=10.128.16.42) 2026-06-08 14:22:50,536 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(raylet, ip=10.128.16.45) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 46x across cluster] +(raylet, ip=10.128.16.45) 2026-06-08 14:22:55,724 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 7x across cluster] +(pid=1438752, ip=10.128.16.45) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1438752, ip=10.128.16.45) No module named 'vllm._version' +(pid=1438752, ip=10.128.16.45) from .version import __version__, __version_tuple__ # isort:skip +(get_addr_port pid=1599106, ip=10.128.16.42) [2026-06-08 14:23:20,554 E 1599106 1599181] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 5x across cluster] +(raylet, ip=10.128.16.36) 2026-06-08 14:22:58,401 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1578474, ip=10.128.16.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1578474, ip=10.128.16.36) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1578474, ip=10.128.16.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(pid=1438752, ip=10.128.16.45) [2026-06-08 14:23:25,742 E 1438752 1438864] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 5x across cluster] +(raylet, ip=10.128.16.47) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1578474, ip=10.128.16.36) [2026-06-08 14:23:28,419 E 1578474 1578514] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(raylet, ip=10.128.16.42) 2026-06-08 14:23:53,837 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(raylet, ip=10.128.16.47) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 29x across cluster] +(raylet, ip=10.128.16.42) 2026-06-08 14:23:53,837 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 2x across cluster] +(pid=1599107, ip=10.128.16.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1599107, ip=10.128.16.42) No module named 'vllm._version' +(pid=1599107, ip=10.128.16.42) from .version import __version__, __version_tuple__ # isort:skip +(raylet, ip=10.128.16.47) 2026-06-08 14:23:59,297 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 2x across cluster] +(pid=1599317, ip=10.128.16.42) [2026-06-08 14:24:23,855 E 1599317 1599427] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(pid=1599318, ip=10.128.16.42) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1599318, ip=10.128.16.42) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1599318, ip=10.128.16.42) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(get_addr_port pid=1621292, ip=10.128.16.47) [2026-06-08 14:24:29,315 E 1621292 1621366] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(pid=1580469, ip=10.128.16.37) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1580469, ip=10.128.16.37) No module named 'vllm._version' +(pid=1580469, ip=10.128.16.37) from .version import __version__, __version_tuple__ # isort:skip +(pid=1621291, ip=10.128.16.47) [2026-06-08 14:24:29,363 E 1621291 1621394] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.47) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(pid=1580614, ip=10.128.16.37) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1580614, ip=10.128.16.37) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1580614, ip=10.128.16.37) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(skyrl_entrypoint pid=1755734) [2026-06-08 14:25:33] INFO inference_engine_client_http_endpoint.py:350: Starting server on 0.0.0.0:8000 +(raylet, ip=10.128.16.47) 2026-06-08 14:25:36,620 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(skyrl_entrypoint pid=1755734) [2026-06-08 14:25:39] INFO inference_engine_client_http_endpoint.py:242: Starting inference HTTP endpoint... +(raylet, ip=10.128.16.47) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 17x across cluster] +(pid=1621493, ip=10.128.16.47) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 4x across cluster] +(pid=1621493, ip=10.128.16.47) No module named 'vllm._version' [repeated 4x across cluster] +(pid=1621493, ip=10.128.16.47) from .version import __version__, __version_tuple__ # isort:skip [repeated 4x across cluster] +(skyrl_entrypoint pid=1755734) [2026-06-08 14:25:39] INFO inference_engine_client_http_endpoint.py:229: Server ready after 7 attempts (7 seconds) +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.979 | INFO  | skyrl_train.inference_engines.inference_engine_client:_spin_up_http_endpoint:969 - InferenceEngineClient HTTP endpoint started on 127.0.0.1:8000 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.979 | INFO  | skyrl_train.inference_engines.inference_engine_client:__init__:61 - InferenceEngineClient initialized with 48 engines. +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.981 | INFO  | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:242 - Harbor logging level set to INFO +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.982 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:142 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 675. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.983 | INFO  | examples.terminal_bench.terminal_bench_generator:__init__:158 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.983 | INFO  | skyrl_train.utils.trainer_utils:build_dataloader:656 - Total steps: 156 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.983 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:357 - Length of train_dataloader: 5000 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.983 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:358 - Number of steps per epoch: 78 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:25:39.983 | INFO  | skyrl_train.fully_async_trainer:_build_train_dataloader_and_compute_training_steps:359 - Total training steps: 80 +(raylet, ip=10.128.16.47) 2026-06-08 14:25:36,620 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. [repeated 2x across cluster] +(raylet, ip=10.128.16.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 7x across cluster] +(pid=1621491, ip=10.128.16.47) [2026-06-08 14:26:06,686 E 1621491 1621629] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(raylet, ip=10.128.16.44) 2026-06-08 14:25:45,355 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(pid=1529125, ip=10.128.16.44) [2026-06-08 14:26:15,373 E 1529125 1529165] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(pid=1529125, ip=10.128.16.44) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead. +(pid=1644759, ip=10.128.16.46) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(pid=1644759, ip=10.128.16.46) No module named 'vllm._version' +(pid=1644759, ip=10.128.16.46) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:36.775 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:36.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:36.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(pid=1645243, ip=10.128.16.46) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(pid=1645243, ip=10.128.16.46) No module named 'vllm._version' [repeated 3x across cluster] +(pid=1645243, ip=10.128.16.46) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:38.053 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:38.079 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:38.079 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:38.079 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:38.079 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) 2026-06-08 14:27:38.226 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.61s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:36.775 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:36.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:36.812 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:38.053 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:38.078 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:38.078 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:38.078 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:38.078 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) 2026-06-08 14:27:38.225 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.66s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:49.527 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:49.533 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:49.533 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) 2026-06-08 14:27:49.527 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=2, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) 2026-06-08 14:27:49.533 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) 2026-06-08 14:27:49.533 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:50.743 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:50.765 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:50.766 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:50.766 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:50.766 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) 2026-06-08 14:27:54.747 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.71s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:49.527 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:49.533 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:49.533 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:50.742 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:50.765 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:50.765 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:50.765 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) 2026-06-08 14:27:50.765 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:57.185 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.44s (attempt 1/5) to avoid port collisions [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:55.678 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=3, VLLM_ENABLE_V1_MULTIPROCESSING= [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:55.809 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:55.809 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:57.044 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:57.074 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:57.074 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:57.074 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) 2026-06-08 14:27:57.074 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 14x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) (EngineCore_DP0 pid=2174122) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) (EngineCore_DP0 pid=2174120) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) [W608 14:30:23.392893112 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-003-37.jupiter.internal]:41891 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) [W608 14:30:23.393734793 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) [rank0]:[W608 14:30:23.400015669 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) (EngineCore_DP0 pid=1288116) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:05.944 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:05.945 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:05.945 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) [W608 14:30:25.743106793 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-001-43.jupiter.internal]:36359 (errno: 97 - Address family not supported by protocol). [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) [W608 14:30:25.744920169 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) [rank0]:[W608 14:30:25.750383305 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:07.184 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:07.208 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:07.208 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:07.208 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:07.208 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:31:07.275 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.42s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:05.945 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:05.945 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:07.190 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:07.208 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:07.208 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:07.208 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:07.208 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:31:07.274 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.67s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:04<00:14, 4.71s/it] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:40.361 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:40.361 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) [W608 14:32:42.294210625 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-001-38.jupiter.internal]:46059 (errno: 97 - Address family not supported by protocol). [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) [W608 14:32:42.298066867 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) [rank0]:[W608 14:32:42.304610351 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:41.597 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:41.622 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:41.622 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:41.622 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:41.622 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) 2026-06-08 14:33:41.736 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.92s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:40.361 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:40.361 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:41.597 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:41.622 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:41.622 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:41.622 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:41.622 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) 2026-06-08 14:33:41.736 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 1.50s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:04<00:12, 4.22s/it] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:25.309 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:25.310 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:26.546 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 0-71 (NUMA node 0) for GPU 0 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:26.572 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 0 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:26.572 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:26.572 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:26.572 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) 2026-06-08 14:34:26.652 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.16s (attempt 1/5) to avoid port collisions +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) Loading safetensors checkpoint shards: 25% Completed | 1/4 [00:04<00:12, 4.26s/it] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:25.309 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:25.310 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:26.546 | INFO | skyrl_train.utils.numa:set_numa_affinity_for_gpu:340 - NUMA affinity: bound process to CPUs 216-287 (NUMA node 3) for GPU 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:26.572 | DEBUG | skyrl_train.utils.numa:_set_membind_via_libnuma:375 - NUMA affinity: set memory preferred to NUMA node 3 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:26.572 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:618 - BaseVLLMInferenceEngine: vllm_v1_disable_multiproc=True, vllm.__version__=dev, VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:26.572 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:__init__:626 - BaseVLLMInferenceEngine: set VLLM_ENABLE_V1_MULTIPROCESSING=0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:26.572 | WARNING | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1192 - OpenAI API sampling params overridden: temperature=0.7, top_p=0.95, top_k=20 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:34:26.652 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1244 - Engine startup stagger: sleeping 2.36s (attempt 1/5) to avoid port collisions [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) Loading safetensors checkpoint shards: 50% Completed | 2/4 [00:08<00:08, 4.12s/it] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) (EngineCore_DP0 pid=3223423) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) (EngineCore_DP0 pid=3223425) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.44s/it] [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) 2026-06-08 14:34:40.939 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) No module named 'vllm._version' +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) from .version import __version__, __version_tuple__ # isort:skip +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) 2026-06-08 14:34:41.036 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) No module named 'vllm._version' [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) from .version import __version__, __version_tuple__ # isort:skip [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) (EngineCore_DP0 pid=1570493) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) (EngineCore_DP0 pid=1570490) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) [W608 14:34:49.767882104 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-001-41-interconnect-1.jupiter.internal]:42099 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) [W608 14:34:49.768300303 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) [rank0]:[W608 14:34:49.773258090 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) 2026-06-08 14:34:50 INFO [ipv4-debug] hostname=jpbo-001-44.jupiter.internal +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) 2026-06-08 14:34:50 INFO [ipv4-debug] _global_node.node_ip_address=10.128.16.44 +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) 2026-06-08 14:34:50 INFO [ipv4-debug] get_node_ip_address()=10.128.16.44 +(raylet, ip=10.128.16.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(skyrl_entrypoint pid=1755734) 2026-06-08 14:34:50.873 | INFO  | skyrl_train.workers.worker:_initiate_actors:636 - Initializing process group for RayActorGroup +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) 2026-06-08 14:34:52.896 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:152 - setup_envvars_for_vllm: distributed_executor_backend=uni, SKYRL_ENABLE_NUMA_AFFINITY=1, CUDA_VISIBLE_DEVICES=0, VLLM_ENABLE_V1_MULTIPROCESSING= +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) 2026-06-08 14:34:52.901 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) 2026-06-08 14:34:52.901 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) 2026-06-08 14:34:55.407 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) 2026-06-08 14:34:55.407 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) 2026-06-08 14:35:01.152 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:167 - setup_envvars_for_vllm: numa_enabled=True [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) 2026-06-08 14:35:01.152 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:setup_envvars_for_vllm:170 - setup_envvars_for_vllm: set VLLM_ENABLE_V1_MULTIPROCESSING=0 for NUMA affinity [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 10.128.16.35 (routable head IP) for coordinator litellm base_url connectivity +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:16.903 | INFO  | examples.terminal_bench.rollout_coordinator:__init__:405 - [RolloutDispatcher] configured num_coordinators=4, cpus_per_coordinator=8 +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) (EngineCore_DP0 pid=1622001) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.) [repeated 7x across cluster] +(raylet, ip=10.128.16.44) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) (EngineCore_DP0 pid=1439417) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) (EngineCore_DP0 pid=1439407) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) (EngineCore_DP0 pid=1651953) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) (EngineCore_DP0 pid=1651949) _C._set_float32_matmul_precision(precision) +(raylet, ip=10.128.16.44) 2026-06-08 14:35:17,800 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) (EngineCore_DP0 pid=1439403) _C._set_float32_matmul_precision(precision) +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:17.982 | INFO  | examples.terminal_bench.rollout_coordinator:startup:434 - [RolloutDispatcher] PlacementGroup ready: 4 bundles x 8 CPU (SPREAD) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) (EngineCore_DP0 pid=1439411) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:13<00:00, 3.29s/it] [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) (EngineCore_DP0 pid=1581284) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) (EngineCore_DP0 pid=1581280) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) [W608 14:35:18.626742094 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-001-48-interconnect-1.jupiter.internal]:34145 (errno: 97 - Address family not supported by protocol). [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) [W608 14:35:18.627240355 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) [rank0]:[W608 14:35:18.629335257 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 23x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) (EngineCore_DP0 pid=1581289) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) 2026-06-08 14:35:20.428 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) _C._set_float32_matmul_precision(precision) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) (EngineCore_DP0 pid=1581280) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 168 (// 4) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) [W608 14:35:23.659956841 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-001-46.jupiter.internal]:35341 (errno: 97 - Address family not supported by protocol). [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) [W608 14:35:24.660430497 Utils.hpp:166] Warning: Environment variable NCCL_BLOCKING_WAIT is deprecated; use TORCH_NCCL_BLOCKING_WAIT instead (function operator()) [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) [rank0]:[W608 14:35:24.662598464 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) [repeated 8x across cluster] +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:25.148 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 1/4 started +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.144 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:242 - Harbor logging level set to INFO +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.144 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:142 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.144 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:158 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.145 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 0/4] constructed (http=10.128.16.35:8000) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.148 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:298 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.148 | INFO | examples.terminal_bench.terminal_bench_generator:startup:255 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=3223698, ip=10.128.16.135) 2026-06-08 14:35:25.148 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 0] startup complete +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) 2026-06-08 14:35:20.543 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00 168 (// 4) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.298 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:242 - Harbor logging level set to INFO +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.299 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:142 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.299 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:158 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.299 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 1/4] constructed (http=10.128.16.35:8000) +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) 2026-06-08 14:35:33.241 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:33.302 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 2/4 started +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.301 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:298 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.301 | INFO | examples.terminal_bench.terminal_bench_generator:startup:255 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=1570740, ip=10.128.16.41) 2026-06-08 14:35:33.302 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 1] startup complete +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) (EngineCore_DP0 pid=1439403) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) (EngineCore_DP0 pid=1439411) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) (EngineCore_DP0 pid=1439417) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) (EngineCore_DP0 pid=1439407) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) (EngineCore_DP0 pid=1645909) Loading safetensors checkpoint shards: 50% Completed | 2/4 [00:08<00:08, 4.27s/it] [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) (EngineCore_DP0 pid=1651953) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) (EngineCore_DP0 pid=1651949) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) (EngineCore_DP0 pid=1581280) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) (EngineCore_DP0 pid=1581289) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) (EngineCore_DP0 pid=1581284) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) 2026-06-08 14:35:38.549 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 12x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.755 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) Loading safetensors checkpoint shards: 75% Completed | 3/4 [00:12<00:04, 4.29s/it] [repeated 27x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.980 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:242 - Harbor logging level set to INFO +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.981 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:142 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.981 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:158 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.981 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 2/4] constructed (http=10.128.16.35:8000) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.984 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:298 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.984 | INFO | examples.terminal_bench.terminal_bench_generator:startup:255 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 14:35:39.984 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 2] startup complete +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:39.985 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 3/4 started +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) (EngineCore_DP0 pid=1645909) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) (EngineCore_DP0 pid=1645905) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e +(raylet, ip=10.128.16.36) 2026-06-08 14:35:43,252 WARNING node.py:1846 -- The object spilling config is specified from an unstable API - system config or environment variable. This is subject to change in the future. You can use the stable API - --object-spilling-directory in ray start or object_spilling_directory in ray.init() to specify the object spilling directory instead. If you need more advanced settings, please open a github issue with the Ray team. +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) 2026-06-08 14:35:43.727 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 8x across cluster] +(bundle_reservation_check_func pid=1529796, ip=10.128.16.44) [2026-06-08 14:35:47,816 E 1529796 1529873] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:14<00:00, 3.55s/it] [repeated 8x across cluster] +(raylet, ip=10.128.16.36) [proxychains] DLL init: proxychains-ng 4.17-git-9-g78ead0e [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:47.851 | INFO | examples.terminal_bench.rollout_coordinator:_scale_terminal_bench_cfg:121 - [RolloutCoordinator] scaled n_concurrent_trials 675 -> 168 (// 4) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.070 | INFO | examples.terminal_bench.terminal_bench_generator:_configure_harbor_logging:242 - Harbor logging level set to INFO +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.070 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:142 - TerminalBenchGenerator initialized with HarborConfigBuilder. Exposed fields: ['name', 'max_episodes', 'enable_summarize', 'store_all_messages', 'trajectory_config', 'enable_episode_logging', 'record_terminal_session', 'enable_pane_logging', 'strict_json_parser', 'interleaved_thinking', 'extra_body', 'override_timeout_sec', 'override_cpus', 'override_memory_mb', 'override_storage_mb', 'auto_snapshot', 'verifier_override_timeout_sec', 'max_retries', 'min_wait_sec', 'max_wait_sec', 'wait_multiplier', 'exclude_exceptions', 'n_concurrent_trials', 'log_level', 'enable_reward_shaping', 'enable_error_classification', 'mask_exceptions', 'default_error_treatment', 'passthrough_exceptions', 'zero_exceptions']. Retry config: max_retries=3, backoff=60.0-600.0s. Concurrent trials: 168. Reward shaping: enabled=False, shaper=pass_ratio. Error classification: enabled=True +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.070 | INFO | examples.terminal_bench.terminal_bench_generator:__init__:158 - TerminalBenchGenerator initialized with custom chat template read from: chat_templates/qwen3_thinking_acc.jinja2 +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.070 | INFO | examples.terminal_bench.rollout_coordinator:__init__:246 - [RolloutCoordinator 3/4] constructed (http=10.128.16.35:8000) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.073 | INFO | examples.terminal_bench.terminal_bench_generator:_create_orchestrator:298 - QueueOrchestrator created and started with n_concurrent_trials=168, rollback_hook registered for ContextLengthExceededError/AgentTimeoutError +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.073 | INFO | examples.terminal_bench.terminal_bench_generator:startup:255 - TerminalBenchGenerator startup complete. Shared orchestrator ready with n_concurrent_trials=168 +(RolloutCoordinator pid=1579300, ip=10.128.16.36) 2026-06-08 14:35:48.073 | INFO | examples.terminal_bench.rollout_coordinator:startup:254 - [RolloutCoordinator 3] startup complete +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.074 | INFO  | examples.terminal_bench.rollout_coordinator:startup:469 - [RolloutDispatcher] coordinator 4/4 started +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.074 | INFO  | examples.terminal_bench.rollout_coordinator:startup:477 - [RolloutDispatcher] 4 coordinators started +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.074 | INFO  | skyrl_train.fully_async_trainer:train:485 - Generator startup complete +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.074 | INFO  | skyrl_train.fully_async_trainer:_train_loop:510 - Started: 'load_checkpoints' +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.154 | INFO  | skyrl_train.trainer:load_checkpoints:1735 - Loading checkpoint from: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_70 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.154 | INFO  | skyrl_train.trainer:load_checkpoints:1741 - Resuming from global_step: 70 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.211 | INFO  | skyrl_train.trainer:load_checkpoints:1757 - Successfully loaded trainer state +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.232 | INFO  | skyrl_train.trainer:load_checkpoints:1767 - Successfully loaded dataloader state +(skyrl_entrypoint pid=1755734) 2026-06-08 14:35:48.233 | INFO  | skyrl_train.trainer:load_checkpoints:1776 - Loading policy checkpoint from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_70/policy +(RolloutCoordinator pid=1579300, ip=10.128.16.36) The tokenizer you are loading from '/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) 2026-06-08 14:35:43.931 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:_create_engine:1301 - Initializing OpenAIServingChat with custom_chat_template read from: chat_templates/qwen3_thinking_acc.jinja2 [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [2026-06-08 14:35:58,153 E 1570740 1570780] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 [repeated 3x across cluster] +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4876: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning. +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) warnings.warn( # warn only once +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.650 | INFO  | skyrl_train.trainer:load_checkpoints:1786 - Successfully loaded policy checkpoint +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.651 | INFO  | skyrl_train.trainer:load_checkpoints:1802 - Successfully loaded complete checkpoint state from global_step_70 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.651 | INFO  | skyrl_train.fully_async_trainer:_train_loop:512 - Resumed training from global_step 70 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.673 | INFO  | skyrl_train.utils.data_tracker:load_state:97 - Loaded data tracker state: epoch=0, consumed_in_epoch=4458, total_consumed=4458 +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.674 | WARNING  | skyrl_train.fully_async_trainer:_train_loop:537 - Data consumption count mismatch on resume: expected 4480, got 4458. This can happen after epoch boundary transitions or error recovery. +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.674 | INFO  | skyrl_train.fully_async_trainer:_train_loop:510 - Finished: 'load_checkpoints', time cost: 16.60s +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:04.674 | INFO  | skyrl_train.fully_async_trainer:_train_loop:544 - Started: 'init_weight_sync_state' +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) No module named 'vllm._version' +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) from .version import __version__, __version_tuple__ # isort:skip +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) 2026-06-08 14:36:10.227 | INFO  | logging:info:2216 - [weight-sync] Using master_addr=10.128.16.44, master_port=54643 +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) 2026-06-08 14:36:10.227 | INFO  | logging:info:2216 - [weight-sync] get_node_ip_address()=10.128.16.44 [repeated 3x across cluster] +(FSDPPolicyWorkerBase pid=1756744) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash: [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=1756744) No module named 'vllm._version' [repeated 7x across cluster] +(FSDPPolicyWorkerBase pid=1756744) from .version import __version__, __version_tuple__ # isort:skip [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) 2026-06-08 14:36:10.385 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:init_weight_update_communicator:223 - torch.distributed.get_rank(): 0, rank_offset: 9, rank: 9, world_size: 49, group_name: skyrl +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) [rank0]:[W608 14:36:10.639802929 socket.cpp:767] [c10d] The client socket cannot be initialized to connect to [jpbo-001-44.jupiter.internal]:54643 (errno: 97 - Address family not supported by protocol). +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) 2026-06-08 14:36:10.389 | INFO | skyrl_train.inference_engines.vllm.vllm_engine:init_weight_update_communicator:234 - init_weight_update_communicator: master_address=10.128.16.44, master_port=54643, +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) /e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4876: UserWarning: barrier(): using the device under current context. You can specify `device_id` in `init_process_group` to mute this warning. +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) warnings.warn( # warn only once +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:10.480 | INFO  | skyrl_train.trainer:init_weight_sync_state:874 - Initialized weight sync state for policy model and inference engines. +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:10.480 | INFO  | skyrl_train.fully_async_trainer:_train_loop:544 - Finished: 'init_weight_sync_state', time cost: 5.81s +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [2026-06-08 14:36:13,270 E 1579300 1579340] core_worker_process.cc:842: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) [rank0]:[W608 14:36:13.158464666 Utils.hpp:137] Warning: Environment variable TORCH_NCCL_TRACE_BUFFER_SIZE is deprecated; use TORCH_FR_BUFFER_SIZE instead (function operator()) +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:17.962 | INFO  | skyrl_train.fully_async_trainer:_train_loop:548 - Finished: 'sync_weights_to_inference_engines', time cost: 7.48s +(skyrl_entrypoint pid=1755734) 2026-06-08 14:36:17.963 | INFO  | skyrl_train.callbacks.builtin:on_train_begin:270 - HFHubUploadCallback initialized: repo=laion/ablation-pymethods2test-seqmean-arm0, upload_steps=5, export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/exports +(skyrl_entrypoint pid=1755734) Training Step Progress: 88%|████████▊ | 70/80 [00:00 cb=[_run_until_complete_cb() at /e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/base_events.py:181]> got Future > attached to a different loop +2026-06-08 16:20:04.069 | ERROR | __main__:main:134 - Training failed: The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. +2026-06-08 16:20:04.069 | INFO | __main__:main:137 - Shutting down Ray on head node... +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 16:20:02.376 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:369 - Shutting down shared QueueOrchestrator... [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 16:20:02.376 | INFO | examples.terminal_bench.terminal_bench_generator:shutdown:371 - QueueOrchestrator shutdown complete [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) 2026-06-08 16:20:02.376 | INFO | examples.terminal_bench.rollout_coordinator:shutdown:258 - [RolloutCoordinator 2] shutdown complete [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Error closing AsyncDaytona client: Task cb=[_run_until_complete_cb() at /e/scratch/jureap59/feuer1/miniforge3/lib/python3.12/asyncio/base_events.py:181]> got Future > attached to a different loop +(skyrl_entrypoint pid=1755734) [fd-monitor] Started monitoring (every 120s) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:08:53] OK: 50 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:08:53] OK: RSS 1.38 GiB | node mem 143.6/858.0 GiB used (16.7%), avail 714.4 GiB +(skyrl_entrypoint pid=1755734) ⚙️ Running in WANDB offline mode +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:10:53] OK: 59 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:10:53] OK: RSS 1.62 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:12:53] OK: 64 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:12:53] OK: RSS 1.62 GiB | node mem 143.7/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:14:53] OK: 68 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:14:53] OK: RSS 1.63 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:16:53] OK: 70 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:16:53] OK: RSS 1.63 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:18:53] OK: 74 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:18:53] OK: RSS 1.63 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:20:53] OK: 76 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:20:53] OK: RSS 1.63 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:22:53] OK: 78 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:22:53] OK: RSS 1.63 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:24:53] OK: 80 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:24:53] OK: RSS 1.63 GiB | node mem 143.8/858.0 GiB used (16.8%), avail 714.2 GiB +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:26:53] OK: 87 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:26:53] OK: RSS 1.63 GiB | node mem 143.7/858.0 GiB used (16.7%), avail 714.3 GiB +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) WARNING 06-08 14:27:44 [arg_utils.py:1256] The global random seed is set to 46. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 14:27:45 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 14:27:45 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 14:27:45 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 14:27:45 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 14:27:45 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) WARNING 06-08 14:27:45 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 14:27:45 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) WARNING 06-08 14:27:46 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) WARNING 06-08 14:27:46 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) WARNING 06-08 14:27:58 [arg_utils.py:1256] The global random seed is set to 42. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 14:27:45 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 14:27:45 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 14:27:45 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 14:27:45 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 14:27:45 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) WARNING 06-08 14:27:45 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 14:27:45 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) WARNING 06-08 14:27:45 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) WARNING 06-08 14:27:45 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) WARNING 06-08 14:28:02 [arg_utils.py:1256] The global random seed is set to 54. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 14:28:04 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 14:28:04 [model.py:1549] Using max model len 32768 [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 14:28:04 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 14:28:04 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 14:28:04 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) WARNING 06-08 14:28:04 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 14:28:04 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) WARNING 06-08 14:28:02 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) WARNING 06-08 14:28:02 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:28:09 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=46, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 14:28:04 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 14:28:04 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 14:28:04 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 14:28:04 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 14:28:04 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) WARNING 06-08 14:28:04 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 14:28:04 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) WARNING 06-08 14:28:05 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) WARNING 06-08 14:28:05 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:28:28 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=42, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:28:41 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=43, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 4x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:28:53] OK: 87 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:28:53] OK: RSS 1.63 GiB | node mem 143.7/858.0 GiB used (16.7%), avail 714.3 GiB +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:28:41 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=54, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:30:22 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:30:23 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.133:41891 backend=nccl +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:30:23 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:30:53] OK: 87 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:30:53] OK: RSS 1.63 GiB | node mem 143.7/858.0 GiB used (16.7%), avail 714.3 GiB +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:30:25 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:30:25 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.43:36359 backend=nccl [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:30:26 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:30:54 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:31:07 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:31:11 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) WARNING 06-08 14:31:16 [arg_utils.py:1256] The global random seed is set to 58. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:07 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:11 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:31:23 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:31:23 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:31:23 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:31:23 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:31:23 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) WARNING 06-08 14:31:23 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:31:23 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) WARNING 06-08 14:31:16 [arg_utils.py:1256] The global random seed is set to 61. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) WARNING 06-08 14:31:23 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) WARNING 06-08 14:31:23 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:31:26 [default_loader.py:293] Loading weights took 14.32 seconds +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:31:26 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 31.121488 seconds +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:31:26 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:29 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:29 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:29 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 14:31:23 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 14:31:23 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 14:31:23 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 14:31:23 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 14:31:23 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) WARNING 06-08 14:31:23 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 14:31:23 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) WARNING 06-08 14:31:23 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) WARNING 06-08 14:31:23 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:30 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:31:30 [core.py:278] init engine (profile, create kv cache, warmup model) took 3.81 seconds +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) WARNING 06-08 14:31:33 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:26 [default_loader.py:293] Loading weights took 14.32 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:26 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 31.124772 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:26 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) WARNING 06-08 14:31:34 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) (EngineCore_DP0 pid=2174120) INFO 06-08 14:31:29 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) (EngineCore_DP0 pid=2174120) INFO 06-08 14:31:29 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) (EngineCore_DP0 pid=2174120) INFO 06-08 14:31:29 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:33 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:33 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) WARNING 06-08 14:31:33 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) (EngineCore_DP0 pid=2174120) INFO 06-08 14:31:30 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) INFO 06-08 14:31:30 [core.py:278] init engine (profile, create kv cache, warmup model) took 3.79 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) (EngineCore_DP0 pid=2174110) WARNING 06-08 14:31:33 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:31:41 [default_loader.py:293] Loading weights took 13.90 seconds +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) WARNING 06-08 14:31:34 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:41 [default_loader.py:293] Loading weights took 13.90 seconds +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:31:41 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 33.548862 seconds +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:44 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:44 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:44 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:31:47 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) INFO 06-08 14:31:47 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) WARNING 06-08 14:31:47 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:44 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:44 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.83 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) (EngineCore_DP0 pid=1288119) WARNING 06-08 14:31:47 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:41 [default_loader.py:293] Loading weights took 13.90 seconds [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:41 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 33.554765 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) WARNING 06-08 14:31:47 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:47 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) WARNING 06-08 14:31:47 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) (EngineCore_DP0 pid=1288118) INFO 06-08 14:31:47 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) WARNING 06-08 14:31:48 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:32:21 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=58, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) WARNING 06-08 14:31:47 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:47 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) WARNING 06-08 14:31:47 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) (EngineCore_DP0 pid=1288117) INFO 06-08 14:31:47 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) WARNING 06-08 14:31:48 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:32:42 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:32:21 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=61, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:32:42 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.38:56675 backend=nccl +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:32:43 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:32:53] OK: 87 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:32:53] OK: RSS 1.63 GiB | node mem 143.7/858.0 GiB used (16.7%), avail 714.3 GiB +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:32:42 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:32:42 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.38:46059 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:32:43 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:33:27 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:33:46 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:33:27 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) WARNING 06-08 14:33:51 [arg_utils.py:1256] The global random seed is set to 62. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:33:46 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:33:54 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:33:54 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:33:54 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:33:54 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:33:54 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) WARNING 06-08 14:33:54 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:33:54 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) WARNING 06-08 14:33:55 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) WARNING 06-08 14:33:55 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) (EngineCore_DP0 pid=1293736) INFO 06-08 14:34:00 [default_loader.py:293] Loading weights took 13.68 seconds +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) WARNING 06-08 14:33:51 [arg_utils.py:1256] The global random seed is set to 65. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:33:54 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:33:54 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:33:54 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:33:54 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:33:54 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) WARNING 06-08 14:33:54 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:33:54 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) WARNING 06-08 14:33:55 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) WARNING 06-08 14:33:55 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:34:00 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 33.187145 seconds +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:34:03 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:34:03 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:34:03 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:34:03 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:34:03 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.81 seconds +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:04 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) WARNING 06-08 14:34:04 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) WARNING 06-08 14:34:04 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:04 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.135:35725 backend=nccl +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:04 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:17 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) (EngineCore_DP0 pid=1293734) INFO 06-08 14:34:00 [default_loader.py:293] Loading weights took 13.68 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:34:04 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:34:04 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) WARNING 06-08 14:34:04 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:34:00 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 33.189826 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) (EngineCore_DP0 pid=1293737) INFO 06-08 14:34:03 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) (EngineCore_DP0 pid=1293737) INFO 06-08 14:34:03 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) (EngineCore_DP0 pid=1293737) INFO 06-08 14:34:03 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) (EngineCore_DP0 pid=1293737) INFO 06-08 14:34:03 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) INFO 06-08 14:34:03 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.79 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:04 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) (EngineCore_DP0 pid=1293735) WARNING 06-08 14:34:04 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) WARNING 06-08 14:34:04 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:04 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.135:33093 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:04 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:23 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:17 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:34:26 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=62, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) WARNING 06-08 14:34:31 [arg_utils.py:1256] The global random seed is set to 67. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:23 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:34:31 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:34:31 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:34:31 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:34:31 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:34:31 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) WARNING 06-08 14:34:31 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:34:31 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) WARNING 06-08 14:34:32 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) WARNING 06-08 14:34:32 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:34:26 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=65, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:37 [default_loader.py:293] Loading weights took 13.76 seconds +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) WARNING 06-08 14:34:31 [arg_utils.py:1256] The global random seed is set to 78. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 14:34:31 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 14:34:31 [model.py:1549] Using max model len 32768 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 14:34:31 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 14:34:31 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 14:34:31 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) WARNING 06-08 14:34:31 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 14:34:31 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:37 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 19.452859 seconds +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) (EngineCore_DP0 pid=3223423) INFO 06-08 14:34:39 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) (EngineCore_DP0 pid=3223423) INFO 06-08 14:34:39 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) (EngineCore_DP0 pid=3223423) INFO 06-08 14:34:39 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) WARNING 06-08 14:34:32 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) WARNING 06-08 14:34:32 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:40 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) INFO 06-08 14:34:40 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.75 seconds +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) (EngineCore_DP0 pid=3223429) WARNING 06-08 14:34:40 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) WARNING 06-08 14:34:40 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:34:49 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:37 [default_loader.py:293] Loading weights took 13.76 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:40 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:40 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:37 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 19.452690 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:39 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:39 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:39 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) WARNING 06-08 14:34:40 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:40 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) INFO 06-08 14:34:40 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.80 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) (EngineCore_DP0 pid=3223424) WARNING 06-08 14:34:40 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) WARNING 06-08 14:34:41 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:34:49 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.41:42099 backend=nccl +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:34:49 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(pid=1529125, ip=10.128.16.44) ⚙️ Running in WANDB offline mode +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:34:50 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:34:52 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:34:53] OK: 88 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:34:53] OK: RSS 1.64 GiB | node mem 143.9/858.0 GiB used (16.8%), avail 714.1 GiB +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:34:54 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=67, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:34:49 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:34:49 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.41:51129 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:34:49 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) WARNING 06-08 14:34:56 [arg_utils.py:1256] The global random seed is set to 83. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:34:50 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:34:56 [model.py:529] Resolved architecture: Qwen3ForCausalLM +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:34:56 [model.py:1549] Using max model len 32768 +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:34:56 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:34:56 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:34:56 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) WARNING 06-08 14:34:56 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:34:56 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) WARNING 06-08 14:34:56 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) WARNING 06-08 14:34:56 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:34:52 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:34:54 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=78, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:00 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(pid=1529475, ip=10.128.16.44) ⚙️ Running in WANDB offline mode +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:01 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.36:53743 backend=nccl +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) WARNING 06-08 14:34:59 [arg_utils.py:1256] The global random seed is set to 89. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:01 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) INFO 06-08 14:34:59 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) INFO 06-08 14:34:59 [model.py:1549] Using max model len 32768 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) INFO 06-08 14:34:59 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) INFO 06-08 14:34:59 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) INFO 06-08 14:34:59 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) WARNING 06-08 14:34:59 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) INFO 06-08 14:34:59 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) WARNING 06-08 14:34:59 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1621493, ip=10.128.16.47) WARNING 06-08 14:34:59 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:01 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) (EngineCore_DP0 pid=1578999) INFO 06-08 14:35:03 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:03 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:06 [default_loader.py:293] Loading weights took 13.48 seconds +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:01 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 3x across cluster] +(pid=1756744) ⚙️ Running in WANDB offline mode [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:01 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.36:35161 backend=nccl [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) WARNING 06-08 14:35:04 [arg_utils.py:1256] The global random seed is set to 74. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:01 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:35:04 [model.py:529] Resolved architecture: Qwen3ForCausalLM [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:35:04 [model.py:1549] Using max model len 32768 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:35:04 [arg_utils.py:1469] Using ray runtime env (env vars redacted): {'env_vars': {'NCCL_CUMEM_ENABLE': '***', 'NCCL_DEBUG': '***', 'NCCL_SOCKET_FAMILY': '***', 'NCCL_SOCKET_IFNAME': '***', 'RAY_ADDRESS': '***', 'VLLM_ALLOW_INSECURE_SERIALIZATION': '***', 'VLLM_ALLOW_RUNTIME_LORA_UPDATING': '***', 'VLLM_DISABLE_COMPILE_CACHE': '***', 'WANDB_API_KEY': '***'}} [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:35:04 [scheduler.py:224] Chunked prefill is enabled with max_num_batched_tokens=65536. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:35:04 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) WARNING 06-08 14:35:04 [vllm.py:728] Enforce eager set, overriding optimization level to -O0 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:35:04 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:06 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.554088 seconds +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) WARNING 06-08 14:35:05 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) WARNING 06-08 14:35:05 [system_utils.py:140] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: In a Ray actor and can only be spawned [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:02 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) (EngineCore_DP0 pid=1599892) INFO 06-08 14:35:09 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=84, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:03 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:09 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:09 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:09 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:09 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) INFO 06-08 14:35:09 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.80 seconds +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) (EngineCore_DP0 pid=1570491) WARNING 06-08 14:35:10 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) WARNING 06-08 14:35:10 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:35:06 [default_loader.py:293] Loading weights took 13.48 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:35:10 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:35:10 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) (EngineCore_DP0 pid=1599892) INFO 06-08 14:35:11 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:35:06 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.556023 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) WARNING 06-08 14:35:10 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) (EngineCore_DP0 pid=1599884) INFO 06-08 14:35:14 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.42:49509 backend=nccl +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) (EngineCore_DP0 pid=1599884) INFO 06-08 14:35:14 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) INFO 06-08 14:35:12 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=53, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) (EngineCore_DP0 pid=1570490) INFO 06-08 14:35:09 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) (EngineCore_DP0 pid=1570490) INFO 06-08 14:35:09 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) (EngineCore_DP0 pid=1570490) INFO 06-08 14:35:09 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) (EngineCore_DP0 pid=1570490) INFO 06-08 14:35:09 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) (EngineCore_DP0 pid=1599884) INFO 06-08 14:35:14 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) INFO 06-08 14:35:09 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.78 seconds [repeated 3x across cluster] +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) NCCL version 2.27.7+cuda13.0 +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) (EngineCore_DP0 pid=1570492) WARNING 06-08 14:35:10 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) WARNING 06-08 14:35:10 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) (EngineCore_DP0 pid=1599892) INFO 06-08 14:35:16 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:16 [default_loader.py:293] Loading weights took 13.16 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) (EngineCore_DP0 pid=1622001) INFO 06-08 14:35:16 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:17 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.701930 seconds +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) INFO 06-08 14:35:18 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.48:34145 backend=nccl [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) INFO 06-08 14:35:18 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) (EngineCore_DP0 pid=1645909) INFO 06-08 14:35:17 [core.py:97] Initializing a V1 LLM engine (vdev) with config: model='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', speculative_config=None, tokenizer='/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=74, served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [65536], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': True, 'static_all_moe_layers': []} [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:19 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:19 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:19 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) (EngineCore_DP0 pid=1578999) INFO 06-08 14:35:19 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) INFO 06-08 14:35:19 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:19 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:19 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.80 seconds +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) WARNING 06-08 14:35:20 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:20 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) WARNING 06-08 14:35:20 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) (EngineCore_DP0 pid=1579000) INFO 06-08 14:35:20 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) WARNING 06-08 14:35:20 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) WARNING 06-08 14:35:20 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) (EngineCore_DP0 pid=1581280) INFO 06-08 14:35:22 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) INFO 06-08 14:35:20 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:17 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.533846 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) (EngineCore_DP0 pid=1645905) INFO 06-08 14:35:24 [parallel_state.py:1234] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.128.16.46:35341 backend=nccl [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) (EngineCore_DP0 pid=1645905) INFO 06-08 14:35:24 [parallel_state.py:1445] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:35:24] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:35:24] OK: RSS 0.78 GiB | node mem 388.7/858.0 GiB used (45.3%), avail 469.2 GiB +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:19 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:19 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:19 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) (EngineCore_DP0 pid=1645905) INFO 06-08 14:35:24 [gpu_model_runner.py:4125] Starting to load model /e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6... [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:19 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:19 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.79 seconds [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) WARNING 06-08 14:35:20 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:20 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) WARNING 06-08 14:35:20 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) (EngineCore_DP0 pid=1579008) INFO 06-08 14:35:20 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) WARNING 06-08 14:35:20 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) INFO 06-08 14:35:29 [default_loader.py:293] Loading weights took 13.21 seconds +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:25 [cuda.py:367] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION']. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) (EngineCore_DP0 pid=1645905) INFO 06-08 14:35:23 [worker_base.py:289] Injected into for extended collective_rpc calls ['_apply_fp8_weight_loader_patches', '_is_fp8_model', '_quantize_weights_for_fp8', '_restore_param_subclasses', '_undo_param_subclasses', 'begin_weight_update', 'destroy_weights_update_group', 'end_weight_update', 'init_weight_update_communicator', 'load_weights', 'read_named_weights', 'set_numa_affinity', 'test_rpc'] [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) INFO 06-08 14:35:29 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 14.381593 seconds +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) (EngineCore_DP0 pid=1599884) INFO 06-08 14:35:32 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) (EngineCore_DP0 pid=1599884) INFO 06-08 14:35:32 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) (EngineCore_DP0 pid=1599884) INFO 06-08 14:35:32 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) (EngineCore_DP0 pid=1599898) INFO 06-08 14:35:32 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) (EngineCore_DP0 pid=1599898) INFO 06-08 14:35:32 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) (EngineCore_DP0 pid=1599898) INFO 06-08 14:35:32 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) INFO 06-08 14:35:32 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) INFO 06-08 14:35:32 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.77 seconds +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:35:33] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:35:33] OK: RSS 0.78 GiB | node mem 378.0/858.0 GiB used (44.1%), avail 480.0 GiB +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) WARNING 06-08 14:35:33 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) INFO 06-08 14:35:33 [vllm.py:690] Asynchronous scheduling is enabled. +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) WARNING 06-08 14:35:33 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) (EngineCore_DP0 pid=1599888) INFO 06-08 14:35:33 [vllm.py:846] Cudagraph is disabled under eager mode +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) WARNING 06-08 14:35:33 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) INFO 06-08 14:35:34 [default_loader.py:293] Loading weights took 13.91 seconds [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) INFO 06-08 14:35:35 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.412189 seconds [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) (EngineCore_DP0 pid=1439411) INFO 06-08 14:35:36 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) (EngineCore_DP0 pid=1439411) INFO 06-08 14:35:36 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) (EngineCore_DP0 pid=1439411) INFO 06-08 14:35:36 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) INFO 06-08 14:35:37 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) (EngineCore_DP0 pid=1439411) INFO 06-08 14:35:36 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.79 seconds [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) WARNING 06-08 14:35:38 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) INFO 06-08 14:35:38 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) WARNING 06-08 14:35:38 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) (EngineCore_DP0 pid=1651959) INFO 06-08 14:35:38 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) WARNING 06-08 14:35:37 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) INFO 06-08 14:35:36 [default_loader.py:293] Loading weights took 14.19 seconds [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:35:39] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:35:39] OK: RSS 0.78 GiB | node mem 186.2/858.0 GiB used (21.7%), avail 671.8 GiB +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) INFO 06-08 14:35:37 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.547768 seconds [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:42 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:42 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:42 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) (EngineCore_DP0 pid=1581289) INFO 06-08 14:35:39 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) (EngineCore_DP0 pid=1581294) INFO 06-08 14:35:39 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.76 seconds [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) WARNING 06-08 14:35:43 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:43 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) WARNING 06-08 14:35:43 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:43 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) WARNING 06-08 14:35:40 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:40 [default_loader.py:293] Loading weights took 14.23 seconds [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] Started monitoring (every 120s) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:35:47] OK: 46 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:35:47] OK: RSS 0.78 GiB | node mem 420.9/858.0 GiB used (49.1%), avail 437.1 GiB +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) (EngineCore_DP0 pid=1645904) INFO 06-08 14:35:40 [gpu_model_runner.py:4222] Model loading took 15.27 GiB memory and 15.695292 seconds [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:42 [gpu_worker.py:373] Available KV cache memory: 49.32 GiB [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:42 [kv_cache_utils.py:1307] GPU KV cache size: 359,104 tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:42 [kv_cache_utils.py:1312] Maximum concurrency for 32,768 tokens per request: 10.96x [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:43 [kernel_warmup.py:44] Skipping FlashInfer autotune because it is disabled. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:43 [core.py:278] init engine (profile, create kv cache, warmup model) took 2.79 seconds [repeated 4x across cluster] +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Loading model from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_70/policy/model_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Loading extra_state from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_70/policy/extra_state_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Loading optim from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_70/policy/optim_world_size_8_rank_0.pt +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Successfully loaded model state dict +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) WARNING 06-08 14:35:43 [serial_utils.py:57] Allowing insecure serialization using pickle due to VLLM_ALLOW_INSECURE_SERIALIZATION=1 [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:43 [vllm.py:690] Asynchronous scheduling is enabled. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) WARNING 06-08 14:35:43 [vllm.py:735] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) (EngineCore_DP0 pid=1645903) INFO 06-08 14:35:43 [vllm.py:846] Cudagraph is disabled under eager mode [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) WARNING 06-08 14:35:43 [model.py:1350] Default vLLM sampling parameters have been overridden by the model's `generation_config.json`: `{'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}`. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. [repeated 4x across cluster] +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Successfully loaded optimizer state +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Successfully loaded scheduler state +(FSDPPolicyWorkerBase pid=1529125, ip=10.128.16.44) [rank-0]: Checkpoint loaded successfully from /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints/global_step_70/policy +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) (EngineCore_DP0 pid=2174114) INFO 06-08 14:36:17 [block_pool.py:452] Successfully reset prefix cache +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) (EngineCore_DP0 pid=1651963) INFO 06-08 14:36:17 [block_pool.py:452] Successfully reset prefix cache [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:36:53] OK: 106 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:36:53] OK: RSS 1.76 GiB | node mem 252.9/858.0 GiB used (29.5%), avail 605.0 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:37:24] OK: 48 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:37:24] OK: RSS 0.96 GiB | node mem 397.5/858.0 GiB used (46.3%), avail 460.5 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:37:33] OK: 48 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:37:33] OK: RSS 0.97 GiB | node mem 386.6/858.0 GiB used (45.1%), avail 471.3 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:37:39] OK: 48 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:37:39] OK: RSS 0.98 GiB | node mem 207.7/858.0 GiB used (24.2%), avail 650.3 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:37:47] OK: 48 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:37:47] OK: RSS 0.98 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:38:53] OK: 106 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:38:53] OK: RSS 1.76 GiB | node mem 252.9/858.0 GiB used (29.5%), avail 605.0 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:39:24] OK: 48 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:39:24] OK: RSS 0.98 GiB | node mem 397.5/858.0 GiB used (46.3%), avail 460.4 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:39:33] OK: 55 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:39:33] OK: RSS 0.98 GiB | node mem 386.6/858.0 GiB used (45.1%), avail 471.3 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 69x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:39:39] OK: 62 / 131,072 FDs open (0.0% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:39:39] OK: RSS 0.99 GiB | node mem 207.7/858.0 GiB used (24.2%), avail 650.2 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:39:47] OK: 72 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:39:47] OK: RSS 0.99 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 39x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 57x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 36x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 39x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 51x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 54x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 36x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 51x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 66x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:40:53] OK: 106 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:40:53] OK: RSS 1.76 GiB | node mem 253.0/858.0 GiB used (29.5%), avail 605.0 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 39x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 51x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 45x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:41:24] OK: 136 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:41:24] OK: RSS 0.99 GiB | node mem 397.5/858.0 GiB used (46.3%), avail 460.5 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 69x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:41:33] OK: 149 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:41:33] OK: RSS 1.00 GiB | node mem 386.7/858.0 GiB used (45.1%), avail 471.3 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 60x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:41:39] OK: 160 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:41:39] OK: RSS 1.00 GiB | node mem 207.8/858.0 GiB used (24.2%), avail 650.2 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 54x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:41:47] OK: 151 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:41:47] OK: RSS 1.00 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 60x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 60x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 51x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 54x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 45x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 69x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 51x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:42:53] OK: 106 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:42:53] OK: RSS 1.76 GiB | node mem 253.0/858.0 GiB used (29.5%), avail 605.0 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 57x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 60x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:43:24] OK: 383 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:43:24] OK: RSS 1.07 GiB | node mem 397.6/858.0 GiB used (46.3%), avail 460.4 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:43:31 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:43:32 [loggers.py:259] Engine 000: Avg prompt throughput: 1.5 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.3%, Prefix cache hit rate: 0.0% +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:43:33] OK: 215 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:43:33] OK: RSS 1.07 GiB | node mem 386.7/858.0 GiB used (45.1%), avail 471.3 GiB +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 14:43:36 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:43:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 44.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.4%, Prefix cache hit rate: 0.0% [repeated 9x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:43:39] OK: 257 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:43:39] OK: RSS 1.11 GiB | node mem 207.8/858.0 GiB used (24.2%), avail 650.1 GiB +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 14:43:41 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:43:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 41.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.4%, Prefix cache hit rate: 0.0% [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 14:43:46 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:43:47 [loggers.py:259] Engine 000: Avg prompt throughput: 74.4 tokens/s, Avg generation throughput: 46.7 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.6%, Prefix cache hit rate: 30.9% [repeated 24x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:43:47] OK: 249 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:43:47] OK: RSS 1.12 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.8 GiB +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 14:43:51 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:43:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 81.2 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.7%, Prefix cache hit rate: 30.9% [repeated 26x across cluster] +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 14:43:53 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:43:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 81.5 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.8%, Prefix cache hit rate: 30.9% [repeated 28x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:43:58 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 81.2 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.9%, Prefix cache hit rate: 30.9% [repeated 30x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 14:43:58 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 45.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.5%, Prefix cache hit rate: 30.9% [repeated 30x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:12 [loggers.py:259] Engine 000: Avg prompt throughput: 111.1 tokens/s, Avg generation throughput: 59.3 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.2%, Prefix cache hit rate: 59.6% [repeated 30x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 80.8 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.3%, Prefix cache hit rate: 59.6% [repeated 30x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 81.0 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.5%, Prefix cache hit rate: 59.6% [repeated 29x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 81.0 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.6%, Prefix cache hit rate: 59.6% [repeated 30x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 14:44:32 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 80.8 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.7%, Prefix cache hit rate: 59.6% [repeated 28x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 80.9 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.8%, Prefix cache hit rate: 59.6% [repeated 29x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 14:44:37 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 72.2 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.9%, Prefix cache hit rate: 59.6% [repeated 30x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 14:44:45 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 70.3 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.0%, Prefix cache hit rate: 59.6% [repeated 34x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:44:49 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 69.2 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.1%, Prefix cache hit rate: 59.6% [repeated 38x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:44:53] OK: 238 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:44:53] OK: RSS 1.77 GiB | node mem 253.0/858.0 GiB used (29.5%), avail 605.0 GiB +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:44:54 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:44:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 85.7 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.2%, Prefix cache hit rate: 59.6% [repeated 43x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:44:57 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 50.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.1%, Prefix cache hit rate: 59.6% [repeated 44x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:45:03 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 45.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.2%, Prefix cache hit rate: 59.6% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 14:45:09 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 45.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.3%, Prefix cache hit rate: 59.6% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:17 [loggers.py:259] Engine 000: Avg prompt throughput: 151.7 tokens/s, Avg generation throughput: 112.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.8%, Prefix cache hit rate: 75.1% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 136.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 3.0%, Prefix cache hit rate: 75.1% [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:45:24] OK: 306 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:45:24] OK: RSS 1.12 GiB | node mem 397.7/858.0 GiB used (46.4%), avail 460.3 GiB +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:45:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 132.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.6%, Prefix cache hit rate: 68.0% [repeated 44x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:45:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 132.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.8%, Prefix cache hit rate: 68.0% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:45:33] OK: 232 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:45:33] OK: RSS 1.11 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.1 GiB +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:45:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 132.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.0%, Prefix cache hit rate: 68.0% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:45:39] OK: 302 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:45:39] OK: RSS 1.13 GiB | node mem 207.9/858.0 GiB used (24.2%), avail 650.1 GiB +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 119.1 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.1%, Prefix cache hit rate: 75.1% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 50.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.7%, Prefix cache hit rate: 75.1% [repeated 44x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:45:47] OK: 293 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:45:47] OK: RSS 1.13 GiB | node mem 430.2/858.0 GiB used (50.1%), avail 427.8 GiB +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 14:45:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 45.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.8%, Prefix cache hit rate: 75.1% [repeated 44x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:45:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 177.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.9%, Prefix cache hit rate: 69.4% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:46:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 170.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.9%, Prefix cache hit rate: 69.4% [repeated 45x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 14:46:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 132.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.1%, Prefix cache hit rate: 69.4% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 14:46:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 89.4 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.2%, Prefix cache hit rate: 30.4% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 89.9 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.6%, Prefix cache hit rate: 82.0% [repeated 44x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:22 [loggers.py:259] Engine 000: Avg prompt throughput: 21.3 tokens/s, Avg generation throughput: 98.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.3%, Prefix cache hit rate: 84.2% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:27 [loggers.py:259] Engine 000: Avg prompt throughput: 152.6 tokens/s, Avg generation throughput: 164.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.5%, Prefix cache hit rate: 85.1% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.3 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.8%, Prefix cache hit rate: 85.1% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 152.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.3%, Prefix cache hit rate: 85.1% [repeated 45x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:42 [loggers.py:259] Engine 000: Avg prompt throughput: 241.9 tokens/s, Avg generation throughput: 151.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.9%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.5% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.4%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:46:53] OK: 294 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:46:53] OK: RSS 1.78 GiB | node mem 253.0/858.0 GiB used (29.5%), avail 605.0 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:46:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.6%, Prefix cache hit rate: 84.5% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.9%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.1%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.3 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.4%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.6%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.9%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:47:24] OK: 310 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:47:24] OK: RSS 1.14 GiB | node mem 397.7/858.0 GiB used (46.4%), avail 460.2 GiB +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 181.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.1%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.4%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:47:33] OK: 307 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:47:33] OK: RSS 1.15 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.1 GiB +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 181.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.6%, Prefix cache hit rate: 84.5% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:47:39] OK: 333 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:47:39] OK: RSS 1.14 GiB | node mem 207.9/858.0 GiB used (24.2%), avail 650.1 GiB +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.9%, Prefix cache hit rate: 84.5% [repeated 45x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:47:47] OK: 325 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:47:47] OK: RSS 1.14 GiB | node mem 430.2/858.0 GiB used (50.1%), avail 427.8 GiB +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.1%, Prefix cache hit rate: 84.5% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 14:47:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.4%, Prefix cache hit rate: 84.5% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:47:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 98.1 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.8%, Prefix cache hit rate: 86.2% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 98.5 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.9%, Prefix cache hit rate: 86.2% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:08 [loggers.py:259] Engine 000: Avg prompt throughput: 22.2 tokens/s, Avg generation throughput: 105.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.8%, Prefix cache hit rate: 87.7% [repeated 45x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 146.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.0%, Prefix cache hit rate: 87.7% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:18 [loggers.py:259] Engine 000: Avg prompt throughput: 109.1 tokens/s, Avg generation throughput: 124.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.5%, Prefix cache hit rate: 87.0% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:48:19 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 147.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.7%, Prefix cache hit rate: 87.0% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:28 [loggers.py:259] Engine 000: Avg prompt throughput: 20.8 tokens/s, Avg generation throughput: 165.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.2%, Prefix cache hit rate: 88.6% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.4%, Prefix cache hit rate: 88.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 14:48:36 [hf.py:318] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:38 [loggers.py:259] Engine 000: Avg prompt throughput: 72.3 tokens/s, Avg generation throughput: 190.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.2%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 196.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.8%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.0%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:48:53] OK: 380 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:48:53] OK: RSS 1.79 GiB | node mem 253.1/858.0 GiB used (29.5%), avail 604.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:48:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 196.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.3%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:49:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 196.3 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.6%, Prefix cache hit rate: 89.3% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:49:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 196.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.9%, Prefix cache hit rate: 89.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:49:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.1%, Prefix cache hit rate: 89.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:49:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.4%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 14:49:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 196.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.7%, Prefix cache hit rate: 89.3% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:49:24] OK: 408 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:49:24] OK: RSS 1.14 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.2 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.9%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:49:33] OK: 415 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:49:33] OK: RSS 1.15 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.0 GiB +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.1%, Prefix cache hit rate: 56.4% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.3%, Prefix cache hit rate: 56.4% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:49:39] OK: 346 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:49:39] OK: RSS 1.14 GiB | node mem 207.9/858.0 GiB used (24.2%), avail 650.0 GiB +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:49:47] OK: 420 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:49:47] OK: RSS 1.14 GiB | node mem 430.2/858.0 GiB used (50.1%), avail 427.8 GiB +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.6%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.8%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:49:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.0%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:50:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.2%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:50:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.4%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 14:50:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 139.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.6%, Prefix cache hit rate: 56.4% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 212.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.6%, Prefix cache hit rate: 88.9% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 154.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.9%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 128.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.1%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 128.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.2%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 128.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.4%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 127.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.6%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 128.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.8%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:50:53] OK: 336 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:50:53] OK: RSS 1.79 GiB | node mem 253.1/858.0 GiB used (29.5%), avail 604.9 GiB +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 14:50:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 128.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.9%, Prefix cache hit rate: 88.9% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:50:59 [loggers.py:259] Engine 000: Avg prompt throughput: 44.3 tokens/s, Avg generation throughput: 178.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.1%, Prefix cache hit rate: 75.7% [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:04 [loggers.py:259] Engine 000: Avg prompt throughput: 111.8 tokens/s, Avg generation throughput: 190.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.9%, Prefix cache hit rate: 75.8% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:09 [loggers.py:259] Engine 000: Avg prompt throughput: 208.0 tokens/s, Avg generation throughput: 261.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.2%, Prefix cache hit rate: 76.0% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 327.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.6%, Prefix cache hit rate: 76.0% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 326.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.1%, Prefix cache hit rate: 76.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 303.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.5%, Prefix cache hit rate: 76.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:51:24] OK: 342 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:51:24] OK: RSS 1.14 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.2 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 281.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.9%, Prefix cache hit rate: 76.0% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:51:33] OK: 367 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:51:33] OK: RSS 1.15 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.0 GiB +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:34 [loggers.py:259] Engine 000: Avg prompt throughput: 75.7 tokens/s, Avg generation throughput: 287.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.4%, Prefix cache hit rate: 75.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:39 [loggers.py:259] Engine 000: Avg prompt throughput: 98.7 tokens/s, Avg generation throughput: 382.7 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.1%, Prefix cache hit rate: 78.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:51:39] OK: 417 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:51:39] OK: RSS 1.14 GiB | node mem 208.0/858.0 GiB used (24.2%), avail 650.0 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 466.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.7%, Prefix cache hit rate: 78.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:51:47] OK: 362 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:51:47] OK: RSS 1.15 GiB | node mem 430.2/858.0 GiB used (50.1%), avail 427.8 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 435.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.2%, Prefix cache hit rate: 78.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:54 [loggers.py:259] Engine 000: Avg prompt throughput: 217.6 tokens/s, Avg generation throughput: 434.8 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.1%, Prefix cache hit rate: 77.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:51:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 563.2 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.9%, Prefix cache hit rate: 77.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:52:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 562.0 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.7%, Prefix cache hit rate: 77.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:52:09 [loggers.py:259] Engine 000: Avg prompt throughput: 83.5 tokens/s, Avg generation throughput: 569.2 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.6%, Prefix cache hit rate: 76.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:52:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 604.5 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.4%, Prefix cache hit rate: 76.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 14:52:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 604.4 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.3%, Prefix cache hit rate: 76.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 14:52:22 [loggers.py:259] Engine 000: Avg prompt throughput: 117.4 tokens/s, Avg generation throughput: 198.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.5%, Prefix cache hit rate: 81.3% [repeated 22x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:52:29 [loggers.py:259] Engine 000: Avg prompt throughput: 297.3 tokens/s, Avg generation throughput: 454.5 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.6%, Prefix cache hit rate: 81.3% [repeated 66x across cluster] +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 14:52:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.8%, Prefix cache hit rate: 89.6% [repeated 54x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:52:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 409.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.8%, Prefix cache hit rate: 81.3% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:52:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 385.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.4%, Prefix cache hit rate: 81.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:52:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 385.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.9%, Prefix cache hit rate: 81.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:52:53] OK: 546 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:52:53] OK: RSS 1.81 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:52:54 [loggers.py:259] Engine 000: Avg prompt throughput: 200.7 tokens/s, Avg generation throughput: 378.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.6%, Prefix cache hit rate: 81.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:52:59 [loggers.py:259] Engine 000: Avg prompt throughput: 385.9 tokens/s, Avg generation throughput: 450.4 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.2%, Prefix cache hit rate: 80.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 460.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.9%, Prefix cache hit rate: 80.6% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:53:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 355.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.0%, Prefix cache hit rate: 81.5% [repeated 43x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:53:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 322.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.4%, Prefix cache hit rate: 81.5% [repeated 52x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:53:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 323.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.9%, Prefix cache hit rate: 81.5% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:53:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 304.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.7%, Prefix cache hit rate: 81.5% [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:53:24] OK: 372 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:53:24] OK: RSS 1.14 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.1 GiB +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 14:53:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 279.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.1%, Prefix cache hit rate: 81.5% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:53:33] OK: 395 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:53:33] OK: RSS 1.16 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.1 GiB +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 402.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.1%, Prefix cache hit rate: 80.6% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 380.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.6%, Prefix cache hit rate: 80.6% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:53:39] OK: 390 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:53:39] OK: RSS 1.14 GiB | node mem 208.0/858.0 GiB used (24.2%), avail 650.0 GiB +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 380.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.2%, Prefix cache hit rate: 80.6% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:53:47] OK: 406 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:53:47] OK: RSS 1.15 GiB | node mem 430.2/858.0 GiB used (50.1%), avail 427.8 GiB +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.7%, Prefix cache hit rate: 80.6% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 380.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.2%, Prefix cache hit rate: 80.6% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 14:53:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 380.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.8%, Prefix cache hit rate: 80.6% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 14:54:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 383.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.8%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 14:54:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.3%, Prefix cache hit rate: 86.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 14:54:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 383.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.9%, Prefix cache hit rate: 86.9% [repeated 46x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 14:54:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 383.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.4%, Prefix cache hit rate: 86.9% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 14:54:24 [loggers.py:259] Engine 000: Avg prompt throughput: 179.5 tokens/s, Avg generation throughput: 408.4 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.6%, Prefix cache hit rate: 86.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 11x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:54:30 [loggers.py:259] Engine 000: Avg prompt throughput: 8.8 tokens/s, Avg generation throughput: 61.0 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.3%, Prefix cache hit rate: 86.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:54:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 59.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.4%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:54:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 49.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.5%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:54:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 49.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.6%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:54:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 49.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.6%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:54:53] OK: 540 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:54:53] OK: RSS 1.81 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:54:55 [loggers.py:259] Engine 000: Avg prompt throughput: 10.0 tokens/s, Avg generation throughput: 92.4 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.8%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 97.9 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.9%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:05 [loggers.py:259] Engine 000: Avg prompt throughput: 95.1 tokens/s, Avg generation throughput: 134.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.8%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 147.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.0%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 14:55:14 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 147.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.2%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:20 [loggers.py:259] Engine 000: Avg prompt throughput: 36.8 tokens/s, Avg generation throughput: 149.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 3.1%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:55:24] OK: 352 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:55:24] OK: RSS 1.14 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.2 GiB +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 194.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 3.4%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 14:55:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 162.9 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 1.7%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:55:33] OK: 352 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:55:33] OK: RSS 1.16 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.0 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:55:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 463.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.4%, Prefix cache hit rate: 89.6% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:55:39] OK: 367 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:55:39] OK: RSS 1.15 GiB | node mem 208.0/858.0 GiB used (24.2%), avail 649.9 GiB +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:55:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 419.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.9%, Prefix cache hit rate: 89.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:55:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 421.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.5%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:55:47] OK: 441 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:55:47] OK: RSS 1.15 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.8 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:55:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 421.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.1%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:55:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 395.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.7%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 14:56:00 [loggers.py:259] Engine 000: Avg prompt throughput: 211.0 tokens/s, Avg generation throughput: 390.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.5%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 458.5 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.9%, Prefix cache hit rate: 82.0% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 455.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.7%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 397.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.3%, Prefix cache hit rate: 82.0% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 368.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.8%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 367.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.4%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 325.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.2%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.7%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.2%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 320.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.6%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:56:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 291.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.1%, Prefix cache hit rate: 82.0% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:56:53] OK: 494 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:56:53] OK: RSS 1.82 GiB | node mem 253.1/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 14:56:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 294.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.6%, Prefix cache hit rate: 87.0% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:57:01 [loggers.py:259] Engine 000: Avg prompt throughput: 68.1 tokens/s, Avg generation throughput: 302.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.7%, Prefix cache hit rate: 82.5% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 14:57:03 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:57:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.2%, Prefix cache hit rate: 82.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:57:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 316.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.6%, Prefix cache hit rate: 82.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 14:57:16 [loggers.py:259] Engine 000: Avg prompt throughput: 87.1 tokens/s, Avg generation throughput: 279.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.1%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:57:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 435.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.0%, Prefix cache hit rate: 84.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 14:57:22 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:57:24] OK: 336 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:57:24] OK: RSS 1.15 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.2 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:57:26 [loggers.py:259] Engine 000: Avg prompt throughput: 121.7 tokens/s, Avg generation throughput: 479.3 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.8%, Prefix cache hit rate: 84.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) ERROR 06-08 14:57:29 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:57:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 506.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.5%, Prefix cache hit rate: 84.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:57:33] OK: 325 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:57:33] OK: RSS 1.16 GiB | node mem 386.9/858.0 GiB used (45.1%), avail 471.1 GiB +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:57:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 424.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.1%, Prefix cache hit rate: 84.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:57:39] OK: 327 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:57:39] OK: RSS 1.15 GiB | node mem 208.1/858.0 GiB used (24.3%), avail 649.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:57:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 422.5 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.6%, Prefix cache hit rate: 84.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:57:46 [loggers.py:259] Engine 000: Avg prompt throughput: 153.3 tokens/s, Avg generation throughput: 431.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.0%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:57:47] OK: 491 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:57:47] OK: RSS 1.16 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.8 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:57:51 [loggers.py:259] Engine 000: Avg prompt throughput: 51.8 tokens/s, Avg generation throughput: 508.4 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.7%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 14:57:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 468.6 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.6%, Prefix cache hit rate: 84.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 483.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.4%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:06 [loggers.py:259] Engine 000: Avg prompt throughput: 91.4 tokens/s, Avg generation throughput: 479.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.8%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 465.2 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.5%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 464.0 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.1%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:21 [loggers.py:259] Engine 000: Avg prompt throughput: 51.5 tokens/s, Avg generation throughput: 474.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) ERROR 06-08 14:58:25 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.4%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 14:58:31 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.9%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.5%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.0%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:46 [loggers.py:259] Engine 000: Avg prompt throughput: 1.4 tokens/s, Avg generation throughput: 386.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.6%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 400.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.4%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:58:53] OK: 521 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [14:58:53] OK: RSS 1.82 GiB | node mem 253.1/858.0 GiB used (29.5%), avail 604.8 GiB +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:58:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 371.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.9%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:59:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.4%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:59:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.0%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 14:59:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 371.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.5%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 318.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.5%, Prefix cache hit rate: 85.1% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 319.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.9%, Prefix cache hit rate: 85.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:59:24] OK: 334 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [14:59:24] OK: RSS 1.15 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.2 GiB +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 319.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.4%, Prefix cache hit rate: 85.1% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 320.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.8%, Prefix cache hit rate: 85.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:59:33] OK: 312 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [14:59:33] OK: RSS 1.16 GiB | node mem 387.0/858.0 GiB used (45.1%), avail 471.0 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 14:59:34 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 318.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.3%, Prefix cache hit rate: 85.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] Error in preprocessing prompt inputs [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] Traceback (most recent call last): [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] return await asyncio.gather( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] raise VLLMValidationError( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 14:59:34 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:59:39] OK: 375 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [14:59:39] OK: RSS 1.16 GiB | node mem 208.1/858.0 GiB used (24.3%), avail 649.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:42 [loggers.py:259] Engine 000: Avg prompt throughput: 112.6 tokens/s, Avg generation throughput: 327.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.7%, Prefix cache hit rate: 85.4% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] Error in preprocessing prompt inputs [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] Traceback (most recent call last): [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] return await asyncio.gather( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] raise VLLMValidationError( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 14:59:42 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:47 [loggers.py:259] Engine 000: Avg prompt throughput: 68.4 tokens/s, Avg generation throughput: 386.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.7%, Prefix cache hit rate: 85.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:59:47] OK: 519 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [14:59:47] OK: RSS 1.17 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.8 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] Error in preprocessing prompt inputs [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] Traceback (most recent call last): [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] return await asyncio.gather( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] raise VLLMValidationError( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) ERROR 06-08 14:59:48 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 14:59:52 [loggers.py:259] Engine 000: Avg prompt throughput: 57.4 tokens/s, Avg generation throughput: 450.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.5%, Prefix cache hit rate: 85.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 14:59:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 360.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.0%, Prefix cache hit rate: 82.3% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 322.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.4%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 322.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 282.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.8%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 276.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.2%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 276.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.6%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 276.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 276.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.3%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 275.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.7%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 276.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.1%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:00:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 277.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.5%, Prefix cache hit rate: 82.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:00:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.3 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.1%, Prefix cache hit rate: 86.6% [repeated 49x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:00:53] OK: 454 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:00:53] OK: RSS 1.82 GiB | node mem 253.1/858.0 GiB used (29.5%), avail 604.8 GiB +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:00:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.7%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:01:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 406.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 53.2%, Prefix cache hit rate: 86.6% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:01:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 53.8%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:01:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.4%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:01:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 406.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.9%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) ERROR 06-08 15:01:20 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621491, ip=10.128.16.47) INFO 06-08 15:01:22 [loggers.py:259] Engine 000: Avg prompt throughput: 2.4 tokens/s, Avg generation throughput: 426.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.6%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:01:24] OK: 460 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:01:24] OK: RSS 1.16 GiB | node mem 397.8/858.0 GiB used (46.4%), avail 460.1 GiB +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] Error in preprocessing prompt inputs [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] Traceback (most recent call last): [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] return await asyncio.gather( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] raise VLLMValidationError( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:01:20 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 313.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.1%, Prefix cache hit rate: 86.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:01:30 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:32 [loggers.py:259] Engine 000: Avg prompt throughput: 1.8 tokens/s, Avg generation throughput: 339.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.7%, Prefix cache hit rate: 86.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:01:33] OK: 365 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:01:33] OK: RSS 1.17 GiB | node mem 387.0/858.0 GiB used (45.1%), avail 471.0 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:01:37 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:37 [loggers.py:259] Engine 000: Avg prompt throughput: 2.8 tokens/s, Avg generation throughput: 376.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.4%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:01:39] OK: 549 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:01:39] OK: RSS 1.18 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.8 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 14x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 14x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] Error in preprocessing prompt inputs [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] Traceback (most recent call last): [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 14x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] return await asyncio.gather( [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] raise VLLMValidationError( [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:01:39 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 403.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.0%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 372.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.1%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:01:47] OK: 704 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:01:47] OK: RSS 1.20 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 359.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.6%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:01:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 357.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.1%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) ERROR 06-08 15:01:57 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 13x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:02:02 [loggers.py:259] Engine 000: Avg prompt throughput: 2.8 tokens/s, Avg generation throughput: 373.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.7%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] Error in preprocessing prompt inputs [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] Traceback (most recent call last): [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] return await asyncio.gather( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] raise VLLMValidationError( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:02:00 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:02:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 402.3 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.2%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:02:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 402.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.8%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:02:16 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:02:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 401.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.4%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:02:19 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 15:02:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 351.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.8%, Prefix cache hit rate: 82.7% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) ERROR 06-08 15:02:24 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 15:02:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 350.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 82.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) ERROR 06-08 15:02:29 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 15:02:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 349.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.8%, Prefix cache hit rate: 82.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 15:02:37 [loggers.py:259] Engine 000: Avg prompt throughput: 23.2 tokens/s, Avg generation throughput: 390.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 83.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 15:02:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 391.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.5%, Prefix cache hit rate: 83.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 15:02:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 355.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.2%, Prefix cache hit rate: 83.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:02:53 [loggers.py:259] Engine 000: Avg prompt throughput: 101.8 tokens/s, Avg generation throughput: 265.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.0%, Prefix cache hit rate: 87.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:02:53] OK: 454 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:02:53] OK: RSS 1.82 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:02:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 275.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.4%, Prefix cache hit rate: 87.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:03 [loggers.py:259] Engine 000: Avg prompt throughput: 91.9 tokens/s, Avg generation throughput: 296.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.4%, Prefix cache hit rate: 87.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:08 [loggers.py:259] Engine 000: Avg prompt throughput: 97.9 tokens/s, Avg generation throughput: 324.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.4%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 368.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.0%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:18 [loggers.py:259] Engine 000: Avg prompt throughput: 119.4 tokens/s, Avg generation throughput: 387.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.3%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 336.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.8%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:03:24] OK: 460 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:03:24] OK: RSS 1.16 GiB | node mem 397.9/858.0 GiB used (46.4%), avail 460.1 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.6%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:03:33] OK: 634 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:03:33] OK: RSS 1.24 GiB | node mem 387.0/858.0 GiB used (45.1%), avail 471.0 GiB +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:03:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.9%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:03:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 307.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.5%, Prefix cache hit rate: 89.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:03:40] OK: 504 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:03:40] OK: RSS 1.20 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.8 GiB +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:03:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 537.3 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 63.2%, Prefix cache hit rate: 78.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:03:47] OK: 676 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:03:47] OK: RSS 1.24 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:03:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 534.3 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 64.0%, Prefix cache hit rate: 78.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:03:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 507.8 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 62.8%, Prefix cache hit rate: 78.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:03:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 474.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.5%, Prefix cache hit rate: 78.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:04:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 447.2 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.2%, Prefix cache hit rate: 78.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:04:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 447.5 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.8%, Prefix cache hit rate: 78.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:04:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 263.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.5%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) ERROR 06-08 15:04:16 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:04:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 263.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.9%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] Error in preprocessing prompt inputs [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] Traceback (most recent call last): [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 64x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] return await asyncio.gather( [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] raise VLLMValidationError( [repeated 32x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:04:18 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 32x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:04:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 263.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.2%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:04:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.6%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:04:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 221.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.9%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:04:35 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 15:04:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 220.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.2%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 12x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:04:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 491.2 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 61.3%, Prefix cache hit rate: 78.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:04:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 488.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 61.9%, Prefix cache hit rate: 78.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:04:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 485.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 62.6%, Prefix cache hit rate: 78.8% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:04:53] OK: 386 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:04:53] OK: RSS 1.82 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:04:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 492.2 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 63.3%, Prefix cache hit rate: 78.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 15:05:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 483.6 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 64.0%, Prefix cache hit rate: 78.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 275.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.1%, Prefix cache hit rate: 88.3% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 245.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.5%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.8%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.1%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:05:24] OK: 583 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:05:24] OK: RSS 1.26 GiB | node mem 398.0/858.0 GiB used (46.4%), avail 460.0 GiB +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.5%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:05:33] OK: 483 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:05:33] OK: RSS 1.26 GiB | node mem 387.0/858.0 GiB used (45.1%), avail 471.0 GiB +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.8%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 245.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.2%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:05:40] OK: 745 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:05:40] OK: RSS 1.21 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.8 GiB +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 245.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.5%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:05:47] OK: 663 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:05:47] OK: RSS 1.23 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:05:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.9%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:05:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.7%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:05:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.1%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.4%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.7%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.1%, Prefix cache hit rate: 87.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 55x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.4%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 18x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 18x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.7%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.0%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) ERROR 06-08 15:06:30 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.3%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) ERROR 06-08 15:06:36 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.7%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:06:37 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.0%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:06:53] OK: 307 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:06:53] OK: RSS 1.82 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.6%, Prefix cache hit rate: 87.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:06:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:07:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.3%, Prefix cache hit rate: 87.6% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:07:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.6%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:07:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.0%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:07:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.3%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:07:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.6%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:07:24] OK: 512 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:07:24] OK: RSS 1.27 GiB | node mem 398.0/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:07:29 [loggers.py:259] Engine 000: Avg prompt throughput: 139.4 tokens/s, Avg generation throughput: 408.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.5%, Prefix cache hit rate: 77.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:07:33] OK: 571 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:07:33] OK: RSS 1.27 GiB | node mem 387.0/858.0 GiB used (45.1%), avail 470.9 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:07:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.1%, Prefix cache hit rate: 77.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:07:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.6%, Prefix cache hit rate: 77.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:07:40] OK: 845 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:07:40] OK: RSS 1.26 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.8 GiB +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:07:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 314.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 82.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:07:47] OK: 637 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:07:47] OK: RSS 1.26 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:07:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.4%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:07:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 314.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.9%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:07:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 314.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.3%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:08:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 314.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.7%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:08:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.2%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:08:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 316.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.6%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:08:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.1%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:08:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.5%, Prefix cache hit rate: 82.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 147.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.2%, Prefix cache hit rate: 88.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.4%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.6%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.8%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.0%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:08:53] OK: 212 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:08:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.2%, Prefix cache hit rate: 88.5% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:08:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.4%, Prefix cache hit rate: 88.5% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.6%, Prefix cache hit rate: 88.5% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:09 [loggers.py:259] Engine 000: Avg prompt throughput: 154.9 tokens/s, Avg generation throughput: 156.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.0%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:14 [loggers.py:259] Engine 000: Avg prompt throughput: 81.7 tokens/s, Avg generation throughput: 193.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.0%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 220.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.0%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 161.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.3%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:09:24] OK: 645 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:09:24] OK: RSS 1.28 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.5%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:09:33] OK: 1,086 / 131,072 FDs open (0.8% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:09:33] OK: RSS 1.33 GiB | node mem 387.1/858.0 GiB used (45.1%), avail 470.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:09:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.7%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:09:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 45.1%, Prefix cache hit rate: 84.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:09:40] OK: 425 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:09:40] OK: RSS 1.31 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:09:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 357.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.0%, Prefix cache hit rate: 84.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:09:47] OK: 818 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:09:47] OK: RSS 1.28 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:09:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 329.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.5%, Prefix cache hit rate: 84.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 15:09:51 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:09:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 329.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.0%, Prefix cache hit rate: 84.1% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:09:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 330.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.4%, Prefix cache hit rate: 84.1% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:10:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 300.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.6%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:10:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 300.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 88.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:10:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 282.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.7%, Prefix cache hit rate: 84.1% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.0%, Prefix cache hit rate: 86.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.3%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 31x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.7%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.1%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.5%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.8%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 41x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.2%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 27x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:10:53] OK: 194 / 131,072 FDs open (0.1% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:10:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:10:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 270.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.6%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:11:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 270.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.0%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:11:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 272.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.4%, Prefix cache hit rate: 86.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 40x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:11:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.7%, Prefix cache hit rate: 86.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:11:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 270.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.1%, Prefix cache hit rate: 86.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:11:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 181.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.2%, Prefix cache hit rate: 84.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:11:24] OK: 1,008 / 131,072 FDs open (0.8% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:11:24] OK: RSS 1.32 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:11:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 271.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.9%, Prefix cache hit rate: 86.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 35x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:11:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 93.5 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.8%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:11:33] OK: 1,025 / 131,072 FDs open (0.8% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:11:33] OK: RSS 1.34 GiB | node mem 387.1/858.0 GiB used (45.1%), avail 470.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:11:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 93.6 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.9%, Prefix cache hit rate: 86.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:11:40] OK: 366 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:11:40] OK: RSS 1.32 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:11:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 93.7 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.0%, Prefix cache hit rate: 86.8% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:11:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 266.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.3%, Prefix cache hit rate: 85.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:11:47] OK: 581 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:11:47] OK: RSS 1.28 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:11:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.1%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:11:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.5%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.8%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.2%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.5%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.9%, Prefix cache hit rate: 63.8% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 252.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.3%, Prefix cache hit rate: 63.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 252.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.6%, Prefix cache hit rate: 63.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 253.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.0%, Prefix cache hit rate: 63.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 252.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 252.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.7%, Prefix cache hit rate: 63.8% [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:12:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 252.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 63.8% [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:12:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 177.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.9%, Prefix cache hit rate: 85.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:12:53] OK: 218 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:12:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:12:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 177.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.2%, Prefix cache hit rate: 85.3% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:13:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 177.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.4%, Prefix cache hit rate: 85.3% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:13:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 178.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.7%, Prefix cache hit rate: 85.3% [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:13:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 258.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.8%, Prefix cache hit rate: 63.8% [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:13:16 [loggers.py:259] Engine 000: Avg prompt throughput: 311.4 tokens/s, Avg generation throughput: 212.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.9%, Prefix cache hit rate: 85.1% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 242.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.3%, Prefix cache hit rate: 87.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 27x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:13:24] OK: 369 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:13:24] OK: RSS 1.32 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:26 [loggers.py:259] Engine 000: Avg prompt throughput: 73.7 tokens/s, Avg generation throughput: 286.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.8%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 288.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.2%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 31x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:13:33] OK: 317 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:13:33] OK: RSS 1.38 GiB | node mem 387.2/858.0 GiB used (45.1%), avail 470.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 288.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.6%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:13:40] OK: 427 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:13:40] OK: RSS 1.32 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 289.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.0%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 289.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.4%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:13:47] OK: 501 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:13:47] OK: RSS 1.32 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 266.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.3%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:13:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 241.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.6%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 54x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:14:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 241.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.9%, Prefix cache hit rate: 87.8% [repeated 45x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:14:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 242.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.3%, Prefix cache hit rate: 87.8% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:14:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 92.1 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 85.8% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:14:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 91.7 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.3%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:14:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 91.7 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.4%, Prefix cache hit rate: 85.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 57x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:14:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 222.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.4%, Prefix cache hit rate: 89.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:14:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 222.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.7%, Prefix cache hit rate: 89.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:14:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 190.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.1%, Prefix cache hit rate: 89.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:14:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 178.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.3%, Prefix cache hit rate: 89.7% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:14:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 178.3 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.6%, Prefix cache hit rate: 89.7% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:14:51 [loggers.py:259] Engine 000: Avg prompt throughput: 64.2 tokens/s, Avg generation throughput: 103.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.9%, Prefix cache hit rate: 86.0% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:14:53] OK: 280 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:14:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:14:56 [loggers.py:259] Engine 000: Avg prompt throughput: 8.8 tokens/s, Avg generation throughput: 140.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.2%, Prefix cache hit rate: 86.0% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:01 [loggers.py:259] Engine 000: Avg prompt throughput: 217.0 tokens/s, Avg generation throughput: 263.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.6%, Prefix cache hit rate: 85.8% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 230.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.0%, Prefix cache hit rate: 85.8% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 230.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.3%, Prefix cache hit rate: 85.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 230.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.6%, Prefix cache hit rate: 85.8% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 230.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.9%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:15:24] OK: 340 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:15:24] OK: RSS 1.32 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 196.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.1%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.3%, Prefix cache hit rate: 85.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:15:33] OK: 375 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:15:33] OK: RSS 1.38 GiB | node mem 387.2/858.0 GiB used (45.1%), avail 470.8 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.6%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:15:40] OK: 403 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:15:40] OK: RSS 1.32 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.9%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:15:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.1%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:15:47] OK: 434 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:15:47] OK: RSS 1.32 GiB | node mem 430.1/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:15:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.5%, Prefix cache hit rate: 86.3% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:15:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.7%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.9%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:07 [loggers.py:259] Engine 000: Avg prompt throughput: 8.8 tokens/s, Avg generation throughput: 144.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 176.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.5%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 167.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.2%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.4%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.6%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.8%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:37 [loggers.py:259] Engine 000: Avg prompt throughput: 80.9 tokens/s, Avg generation throughput: 170.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.1%, Prefix cache hit rate: 86.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 177.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.4%, Prefix cache hit rate: 86.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 15:16:47 [loggers.py:259] Engine 000: Avg prompt throughput: 11.6 tokens/s, Avg generation throughput: 210.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.8%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 15:16:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 332.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.3%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:16:53] OK: 388 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:16:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:16:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.0%, Prefix cache hit rate: 87.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:17:02 [loggers.py:259] Engine 000: Avg prompt throughput: 67.0 tokens/s, Avg generation throughput: 235.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.6%, Prefix cache hit rate: 87.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:17:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 279.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.0%, Prefix cache hit rate: 87.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:17:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 266.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.9%, Prefix cache hit rate: 87.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:17:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 233.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.3%, Prefix cache hit rate: 87.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:22 [loggers.py:259] Engine 000: Avg prompt throughput: 258.2 tokens/s, Avg generation throughput: 473.0 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.9%, Prefix cache hit rate: 88.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:17:24] OK: 384 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:17:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 506.4 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.3%, Prefix cache hit rate: 88.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:32 [loggers.py:259] Engine 000: Avg prompt throughput: 123.0 tokens/s, Avg generation throughput: 542.0 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.4%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 33x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:17:33] OK: 421 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:17:33] OK: RSS 1.38 GiB | node mem 387.2/858.0 GiB used (45.1%), avail 470.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:37 [loggers.py:259] Engine 000: Avg prompt throughput: 9.2 tokens/s, Avg generation throughput: 618.7 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.4%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:17:40] OK: 359 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:17:40] OK: RSS 1.32 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 585.4 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.1%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 547.9 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.9%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:17:47] OK: 411 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:17:47] OK: RSS 1.32 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 427.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 547.7 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.6%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 41x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:17:57 [loggers.py:259] Engine 000: Avg prompt throughput: 117.5 tokens/s, Avg generation throughput: 562.2 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.9%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:18:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 627.2 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.3%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 31x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:18:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 559.9 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.1%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 371.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.2%, Prefix cache hit rate: 85.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:17 [loggers.py:259] Engine 000: Avg prompt throughput: 9.2 tokens/s, Avg generation throughput: 351.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.8%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.3%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.9%, Prefix cache hit rate: 85.7% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.4%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.5%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 343.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.2%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 287.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.0%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:18:53] OK: 505 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:18:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.8 GiB +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:18:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 240.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.4%, Prefix cache hit rate: 85.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 258.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.8%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 258.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.2%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:12 [loggers.py:259] Engine 000: Avg prompt throughput: 22.3 tokens/s, Avg generation throughput: 259.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.1%, Prefix cache hit rate: 88.3% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 300.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.5%, Prefix cache hit rate: 88.3% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 268.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.3%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:19:24] OK: 428 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:19:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 257.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.6%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 258.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.0%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:19:33] OK: 402 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:19:33] OK: RSS 1.38 GiB | node mem 387.3/858.0 GiB used (45.1%), avail 470.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 257.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.4%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:19:40] OK: 321 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:19:40] OK: RSS 1.32 GiB | node mem 208.3/858.0 GiB used (24.3%), avail 649.7 GiB +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 257.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.7%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:19:47] OK: 424 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:19:47] OK: RSS 1.32 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 427.9 GiB +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 257.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.1%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:19:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 257.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.4%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:19:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 318.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.7%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:20:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 319.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.2%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438752, ip=10.128.16.45) INFO 06-08 15:20:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 411.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.2%, Prefix cache hit rate: 86.2% [repeated 40x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 325.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.6%, Prefix cache hit rate: 88.6% [repeated 72x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 322.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.1%, Prefix cache hit rate: 88.6% [repeated 52x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 322.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.5%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.0%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 323.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.4%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 323.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.9%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:20:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 324.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.3%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:20:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 407.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.7%, Prefix cache hit rate: 87.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:20:53] OK: 517 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:20:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.7 GiB +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:20:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 408.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.3%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:21:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 407.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.8%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:21:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 407.4 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.4%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:21:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 408.5 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.0%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:21:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 408.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.5%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:21:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 406.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.1%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:21:24] OK: 379 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:21:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:21:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 639.5 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 72.8%, Prefix cache hit rate: 80.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:21:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 624.5 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 73.7%, Prefix cache hit rate: 80.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:21:33] OK: 345 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:21:33] OK: RSS 1.38 GiB | node mem 387.2/858.0 GiB used (45.1%), avail 470.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:21:36 [loggers.py:259] Engine 000: Avg prompt throughput: 3.0 tokens/s, Avg generation throughput: 641.7 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 74.6%, Prefix cache hit rate: 80.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:21:40] OK: 328 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:21:40] OK: RSS 1.32 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:21:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 650.8 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 66.6%, Prefix cache hit rate: 80.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:21:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 624.6 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 67.5%, Prefix cache hit rate: 80.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:21:47] OK: 439 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:21:47] OK: RSS 1.33 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:21:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 626.3 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 68.4%, Prefix cache hit rate: 80.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:21:56 [loggers.py:259] Engine 000: Avg prompt throughput: 61.9 tokens/s, Avg generation throughput: 358.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.0%, Prefix cache hit rate: 83.1% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:22:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 447.4 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.9%, Prefix cache hit rate: 88.0%[32m [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 287.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.7%, Prefix cache hit rate: 86.7% [repeated 51x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 261.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.1%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 261.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.5%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 261.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.8%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 261.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.2%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:22:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 380.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.2%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:22:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 367.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.7%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:22:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 366.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.2%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:22:44 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:46 [loggers.py:259] Engine 000: Avg prompt throughput: 67.8 tokens/s, Avg generation throughput: 286.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.5%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:51 [loggers.py:259] Engine 000: Avg prompt throughput: 368.4 tokens/s, Avg generation throughput: 348.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.3%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:22:53] OK: 526 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:22:53] OK: RSS 1.83 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:22:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 346.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.9%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:23:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 366.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.2%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:23:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 365.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.8%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:23:09 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:23:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 401.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.4%, Prefix cache hit rate: 83.7% [repeated 37x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.5%, Prefix cache hit rate: 86.7% [repeated 62x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.0%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:23:24] OK: 324 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:23:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.4%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.9%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:23:33] OK: 319 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:23:33] OK: RSS 1.38 GiB | node mem 387.2/858.0 GiB used (45.1%), avail 470.7 GiB +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:23:40] OK: 325 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:23:40] OK: RSS 1.32 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 316.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.7%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.2%, Prefix cache hit rate: 86.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:23:47] OK: 452 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:23:47] OK: RSS 1.32 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 427.9 GiB +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.6%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:23:57 [loggers.py:259] Engine 000: Avg prompt throughput: 108.6 tokens/s, Avg generation throughput: 334.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.7%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:24:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 712.3 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 68.6%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:24:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 715.0 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 69.6%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.6%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) ERROR 06-08 15:24:15 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.8%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.0%, Prefix cache hit rate: 86.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 140.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.2%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.4%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.2 tokens/s, Avg generation throughput: 184.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:24:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 187.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.9%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:24:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 345.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.6%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:24:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 345.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.1%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:24:53] OK: 513 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:24:53] OK: RSS 1.84 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:24:57 [loggers.py:259] Engine 000: Avg prompt throughput: 9.8 tokens/s, Avg generation throughput: 352.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.4%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 430.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.1%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:07 [loggers.py:259] Engine 000: Avg prompt throughput: 106.4 tokens/s, Avg generation throughput: 436.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:25:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 433.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.2%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 385.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.8%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.4%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:25:24] OK: 323 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:25:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 385.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.9%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.4%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:25:33] OK: 339 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:25:33] OK: RSS 1.38 GiB | node mem 387.2/858.0 GiB used (45.1%), avail 470.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.0%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:25:40] OK: 466 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:25:40] OK: RSS 1.33 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:42 [loggers.py:259] Engine 000: Avg prompt throughput: 52.5 tokens/s, Avg generation throughput: 423.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.3%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:47 [loggers.py:259] Engine 000: Avg prompt throughput: 87.5 tokens/s, Avg generation throughput: 447.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.4%, Prefix cache hit rate: 87.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:25:47] OK: 627 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:25:47] OK: RSS 1.32 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 432.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.0%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:25:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 416.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.2%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:26:00 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:26:02 [loggers.py:259] Engine 000: Avg prompt throughput: 132.3 tokens/s, Avg generation throughput: 374.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.8%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:26:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 385.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:26:12 [loggers.py:259] Engine 000: Avg prompt throughput: 76.1 tokens/s, Avg generation throughput: 415.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.2%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:26:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 410.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.4%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:26:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.0%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:26:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 385.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.5%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:26:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 184.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.5%, Prefix cache hit rate: 87.7% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 11x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:26:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.8%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:26:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 184.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.0%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:26:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 184.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.3%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:26:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 158.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.5%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:26:53] OK: 464 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:26:53] OK: RSS 1.84 GiB | node mem 253.2/858.0 GiB used (29.5%), avail 604.7 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:26:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.3 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.7%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.9%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.1%, Prefix cache hit rate: 87.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:13 [loggers.py:259] Engine 000: Avg prompt throughput: 8.8 tokens/s, Avg generation throughput: 168.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.3%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.5%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) ERROR 06-08 15:27:22 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.7%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:27:24] OK: 315 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:27:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.9%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:27:33] OK: 340 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:27:33] OK: RSS 1.38 GiB | node mem 387.3/858.0 GiB used (45.1%), avail 470.7 GiB +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.1%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.3%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:27:40] OK: 361 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:27:40] OK: RSS 1.33 GiB | node mem 208.3/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) ERROR 06-08 15:27:40 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 11x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.5%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:27:47 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:27:47] OK: 465 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:27:47] OK: RSS 1.33 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:48 [loggers.py:259] Engine 000: Avg prompt throughput: 2.6 tokens/s, Avg generation throughput: 181.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:53 [loggers.py:259] Engine 000: Avg prompt throughput: 262.8 tokens/s, Avg generation throughput: 208.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.2%, Prefix cache hit rate: 87.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:27:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 205.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.2%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 182.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.5%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:08 [loggers.py:259] Engine 000: Avg prompt throughput: 82.7 tokens/s, Avg generation throughput: 204.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.1%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 229.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.4%, Prefix cache hit rate: 88.0% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 206.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.3%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 184.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.5%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 177.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.9%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) ERROR 06-08 15:28:32 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:33 [loggers.py:259] Engine 000: Avg prompt throughput: 21.1 tokens/s, Avg generation throughput: 148.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.5%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:38 [loggers.py:259] Engine 000: Avg prompt throughput: 9.8 tokens/s, Avg generation throughput: 206.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.7%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.5%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:28:48 [loggers.py:259] Engine 000: Avg prompt throughput: 23.2 tokens/s, Avg generation throughput: 152.3 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.2%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:28:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 400.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.6%, Prefix cache hit rate: 91.0% [repeated 47x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:28:53] OK: 438 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:28:53] OK: RSS 1.84 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:28:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 825.3 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.3%, Prefix cache hit rate: 65.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 815.2 tokens/s, Running: 22 reqs, Waiting: 0 reqs, GPU KV cache usage: 85.6%, Prefix cache hit rate: 65.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 787.8 tokens/s, Running: 22 reqs, Waiting: 0 reqs, GPU KV cache usage: 86.7%, Prefix cache hit rate: 65.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 789.2 tokens/s, Running: 22 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.8%, Prefix cache hit rate: 65.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 790.2 tokens/s, Running: 22 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.9%, Prefix cache hit rate: 65.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:23 [loggers.py:259] Engine 000: Avg prompt throughput: 3313.9 tokens/s, Avg generation throughput: 729.6 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.8%, Prefix cache hit rate: 63.7% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:29:24] OK: 486 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:29:24] OK: RSS 1.33 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 828.4 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.0%, Prefix cache hit rate: 63.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:29:33] OK: 380 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:29:33] OK: RSS 1.38 GiB | node mem 387.3/858.0 GiB used (45.1%), avail 470.7 GiB +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:33 [loggers.py:259] Engine 000: Avg prompt throughput: 1.6 tokens/s, Avg generation throughput: 849.8 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.2%, Prefix cache hit rate: 63.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 861.1 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.4%, Prefix cache hit rate: 63.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:29:40] OK: 774 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:29:40] OK: RSS 1.33 GiB | node mem 208.2/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 862.0 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.6%, Prefix cache hit rate: 63.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:29:47] OK: 472 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:29:47] OK: RSS 1.33 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 859.9 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 96.8%, Prefix cache hit rate: 63.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:29:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 840.3 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.1%, Prefix cache hit rate: 63.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:29:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.4%, Prefix cache hit rate: 88.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 861.4 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 98.4%, Prefix cache hit rate: 63.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 15:30:05 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 860.9 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 99.6%, Prefix cache hit rate: 63.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:30:08 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 834.1 tokens/s, Running: 23 reqs, Waiting: 2 reqs, GPU KV cache usage: 99.7%, Prefix cache hit rate: 63.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 777.8 tokens/s, Running: 21 reqs, Waiting: 4 reqs, GPU KV cache usage: 97.7%, Prefix cache hit rate: 63.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:23 [loggers.py:259] Engine 000: Avg prompt throughput: 3055.9 tokens/s, Avg generation throughput: 712.7 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 89.4%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 825.8 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.6%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 824.0 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 91.7%, Prefix cache hit rate: 61.5% [repeated 36x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 824.7 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.9%, Prefix cache hit rate: 61.5% [repeated 60x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 824.0 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.0%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 806.2 tokens/s, Running: 22 reqs, Waiting: 0 reqs, GPU KV cache usage: 86.3%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:30:53] OK: 367 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:30:53] OK: RSS 1.85 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.7 GiB +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 769.5 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 85.9%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) ERROR 06-08 15:30:55 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:30:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 753.7 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.0%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) ERROR 06-08 15:31:00 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:31:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 729.7 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.2%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 15:31:05 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:31:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 702.0 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 79.3%, Prefix cache hit rate: 61.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:31:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 681.8 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 80.3%, Prefix cache hit rate: 61.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:31:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 681.0 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 81.2%, Prefix cache hit rate: 61.5% [repeated 31x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.2%, Prefix cache hit rate: 89.4% [repeated 66x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:31:24] OK: 675 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:31:24] OK: RSS 1.34 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.8 GiB +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.5%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:31:33] OK: 528 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:31:33] OK: RSS 1.38 GiB | node mem 387.3/858.0 GiB used (45.1%), avail 470.7 GiB +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.7%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.0%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:31:40] OK: 502 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:31:40] OK: RSS 1.35 GiB | node mem 208.3/858.0 GiB used (24.3%), avail 649.7 GiB +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.2%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:31:47] OK: 664 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:31:47] OK: RSS 1.33 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.5%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.7%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:31:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.0%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.2%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.5%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 89.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.0%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.2%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) ERROR 06-08 15:32:27 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:29 [loggers.py:259] Engine 000: Avg prompt throughput: 111.4 tokens/s, Avg generation throughput: 185.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.0%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:32:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.4%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:32:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 621.0 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 76.4%, Prefix cache hit rate: 60.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:32:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 620.8 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 77.2%, Prefix cache hit rate: 60.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:32:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 618.9 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 78.1%, Prefix cache hit rate: 60.1% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:32:53] OK: 254 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:32:53] OK: RSS 1.85 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.7 GiB +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 15:32:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 662.9 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 70.1%, Prefix cache hit rate: 60.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:32:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 91.6 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.1%, Prefix cache hit rate: 88.9% [repeated 22x across cluster] +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 142.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.1%, Prefix cache hit rate: 89.2% [repeated 70x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.3%, Prefix cache hit rate: 89.2% [repeated 54x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.5%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.7%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 142.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.8%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:33:24] OK: 729 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:33:24] OK: RSS 1.34 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.9 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.0%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:33:33] OK: 590 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:33:33] OK: RSS 1.38 GiB | node mem 387.3/858.0 GiB used (45.1%), avail 470.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.2%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621291, ip=10.128.16.47) INFO 06-08 15:33:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 141.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.4%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:33:40] OK: 373 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:33:40] OK: RSS 1.35 GiB | node mem 208.3/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 15:33:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 237.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.8%, Prefix cache hit rate: 88.8% [repeated 27x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:33:47] OK: 812 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:33:47] OK: RSS 1.33 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:33:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 378.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 47.6%, Prefix cache hit rate: 90.7% [repeated 71x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:33:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 378.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 48.1%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:33:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 379.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 48.7%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 378.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 49.2%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:34:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 429.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.4%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 377.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.2%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 376.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.8%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 377.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.3%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 376.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.8%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.3%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 376.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.9%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 53.4%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.3 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 53.9%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:34:53] OK: 210 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:34:53] OK: RSS 1.85 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.4%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 15:34:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.0%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:35:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 428.4 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 62.0%, Prefix cache hit rate: 87.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:35:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 428.5 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 62.6%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:35:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 428.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 63.2%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:35:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 399.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.9%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:35:24] OK: 1,015 / 131,072 FDs open (0.8% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:35:24] OK: RSS 1.36 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.8 GiB +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:35:25 [loggers.py:259] Engine 000: Avg prompt throughput: 2.0 tokens/s, Avg generation throughput: 408.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 56.3%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 15:35:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 426.7 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 56.9%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:35:33] OK: 907 / 131,072 FDs open (0.7% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:35:33] OK: RSS 1.44 GiB | node mem 387.4/858.0 GiB used (45.1%), avail 470.6 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:35:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 236.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 87.9% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:35:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 236.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.0%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:35:40] OK: 364 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:35:40] OK: RSS 1.35 GiB | node mem 208.3/858.0 GiB used (24.3%), avail 649.7 GiB +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 15:35:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 197.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.6%, Prefix cache hit rate: 87.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:35:47] OK: 815 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:35:47] OK: RSS 1.34 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:35:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 182.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.1%, Prefix cache hit rate: 90.9% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:35:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.3%, Prefix cache hit rate: 90.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:36:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.6%, Prefix cache hit rate: 90.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:36:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 182.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.9%, Prefix cache hit rate: 90.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:36:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 182.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.1%, Prefix cache hit rate: 90.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578320, ip=10.128.16.36) INFO 06-08 15:36:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 183.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.4%, Prefix cache hit rate: 90.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:36:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 174.3 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.9%, Prefix cache hit rate: 87.3% [repeated 20x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 15:36:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 171.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.6%, Prefix cache hit rate: 88.6% [repeated 84x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 15:36:31 [loggers.py:259] Engine 000: Avg prompt throughput: 128.4 tokens/s, Avg generation throughput: 210.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.8%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 15:36:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 215.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.1%, Prefix cache hit rate: 88.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 41x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 15:36:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 326.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.3%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 15:36:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 324.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.7%, Prefix cache hit rate: 89.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 15:36:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 325.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.2%, Prefix cache hit rate: 89.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:36:53] OK: 203 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:36:53] OK: RSS 1.85 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 15:36:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 324.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.6%, Prefix cache hit rate: 89.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 15:37:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 325.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.1%, Prefix cache hit rate: 89.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 15:37:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 230.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.9%, Prefix cache hit rate: 89.9% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 64.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.5%, Prefix cache hit rate: 88.3% [repeated 59x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 44.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.6%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 44.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.6%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:37:24] OK: 1,147 / 131,072 FDs open (0.9% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:37:24] OK: RSS 1.37 GiB | node mem 398.1/858.0 GiB used (46.4%), avail 459.8 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 44.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.7%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 44.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.8%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:37:33] OK: 456 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:37:33] OK: RSS 1.42 GiB | node mem 387.4/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 43.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.8%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:37:40] OK: 388 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:37:40] OK: RSS 1.36 GiB | node mem 208.3/858.0 GiB used (24.3%), avail 649.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 15:37:42 [loggers.py:259] Engine 000: Avg prompt throughput: 46.8 tokens/s, Avg generation throughput: 86.4 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.0%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:37:47] OK: 458 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:37:47] OK: RSS 1.34 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:37:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.7%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:37:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 90.7% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:37:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.3%, Prefix cache hit rate: 90.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.6%, Prefix cache hit rate: 90.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 231.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.0%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.3%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.6%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:23 [loggers.py:259] Engine 000: Avg prompt throughput: 76.5 tokens/s, Avg generation throughput: 238.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.0%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 276.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.4%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 27x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 278.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.8%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 278.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.2%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 278.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.6%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 278.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.0%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:53 [loggers.py:259] Engine 000: Avg prompt throughput: 7.6 tokens/s, Avg generation throughput: 303.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.5%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:38:53] OK: 260 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:38:53] OK: RSS 1.85 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:38:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 324.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.0%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:39:03 [loggers.py:259] Engine 000: Avg prompt throughput: 8.8 tokens/s, Avg generation throughput: 338.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.6%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:39:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 369.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.1%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 33x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:39:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 593.5 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.9%, Prefix cache hit rate: 81.0% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) INFO 06-08 15:39:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 539.2 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.7%, Prefix cache hit rate: 81.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438751, ip=10.128.16.45) INFO 06-08 15:39:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 320.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.0%, Prefix cache hit rate: 89.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:39:24] OK: 380 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:39:24] OK: RSS 1.44 GiB | node mem 398.2/858.0 GiB used (46.4%), avail 459.8 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 229.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.5%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:39:33] OK: 304 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:39:33] OK: RSS 1.42 GiB | node mem 387.4/858.0 GiB used (45.2%), avail 470.6 GiB +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 223.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.8%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.1%, Prefix cache hit rate: 87.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:39:40] OK: 421 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:39:40] OK: RSS 1.36 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.5%, Prefix cache hit rate: 87.0% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:39:47] OK: 412 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:39:47] OK: RSS 1.34 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.8%, Prefix cache hit rate: 87.0% [repeated 46x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 40x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.1%, Prefix cache hit rate: 87.0% [repeated 45x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:39:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.4%, Prefix cache hit rate: 87.0% [repeated 45x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.7%, Prefix cache hit rate: 87.0% [repeated 45x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.0%, Prefix cache hit rate: 87.0% [repeated 45x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.3%, Prefix cache hit rate: 87.0% [repeated 45x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.7%, Prefix cache hit rate: 87.0% [repeated 45x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:23 [loggers.py:259] Engine 000: Avg prompt throughput: 114.5 tokens/s, Avg generation throughput: 242.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.7%, Prefix cache hit rate: 86.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 268.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.1%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:40:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 221.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.9%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:40:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.2%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:40:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 190.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.5%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 206.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.7%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:40:53] OK: 273 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:40:53] OK: RSS 1.85 GiB | node mem 253.4/858.0 GiB used (29.5%), avail 604.6 GiB +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.9%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:40:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.2%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:41:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.4%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:41:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 86.9% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:41:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.1%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 32x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:41:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.3%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:41:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.6%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:41:25] OK: 335 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:41:25] OK: RSS 1.44 GiB | node mem 398.2/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 33x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:41:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.8%, Prefix cache hit rate: 86.7% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:41:33] OK: 333 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:41:33] OK: RSS 1.42 GiB | node mem 387.4/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222697, ip=10.128.16.135) INFO 06-08 15:41:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 189.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.1%, Prefix cache hit rate: 86.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:41:39 [loggers.py:259] Engine 000: Avg prompt throughput: 155.7 tokens/s, Avg generation throughput: 300.1 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.3%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:41:40] OK: 421 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:41:40] OK: RSS 1.36 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:41:44 [loggers.py:259] Engine 000: Avg prompt throughput: 8.0 tokens/s, Avg generation throughput: 390.7 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.9%, Prefix cache hit rate: 88.3% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:41:47] OK: 489 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:41:47] OK: RSS 1.34 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:41:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 379.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.8%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 37x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:41:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 335.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 23.9%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 38x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:41:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 130.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.2%, Prefix cache hit rate: 87.4% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 37x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:04 [loggers.py:259] Engine 000: Avg prompt throughput: 8.6 tokens/s, Avg generation throughput: 164.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 2.6%, Prefix cache hit rate: 87.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 34x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:09 [loggers.py:259] Engine 000: Avg prompt throughput: 169.1 tokens/s, Avg generation throughput: 211.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.4%, Prefix cache hit rate: 87.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 39x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:14 [loggers.py:259] Engine 000: Avg prompt throughput: 159.5 tokens/s, Avg generation throughput: 273.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.4%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:19 [loggers.py:259] Engine 000: Avg prompt throughput: 263.0 tokens/s, Avg generation throughput: 253.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.4%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 236.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.4%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 216.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 4.7%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 201.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 3.3%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 172.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 3.6%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 32x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:44 [loggers.py:259] Engine 000: Avg prompt throughput: 212.7 tokens/s, Avg generation throughput: 251.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.0%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 259.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.3%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 38x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:42:53] OK: 380 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:42:53] OK: RSS 1.85 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 259.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.7%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:42:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 239.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.0%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 52x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:04 [loggers.py:259] Engine 000: Avg prompt throughput: 30.7 tokens/s, Avg generation throughput: 245.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.8%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 249.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.7%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 185.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.3%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 32x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:19 [loggers.py:259] Engine 000: Avg prompt throughput: 189.8 tokens/s, Avg generation throughput: 209.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.8%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 216.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.1%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:43:25] OK: 322 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:43:25] OK: RSS 1.44 GiB | node mem 398.2/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 191.4 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 6.8%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:43:33] OK: 402 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:43:33] OK: RSS 1.42 GiB | node mem 387.4/858.0 GiB used (45.2%), avail 470.6 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:34 [loggers.py:259] Engine 000: Avg prompt throughput: 168.0 tokens/s, Avg generation throughput: 221.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.6%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 259.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.0%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:43:40] OK: 383 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:43:40] OK: RSS 1.36 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 259.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.4%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:43:47] OK: 422 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:43:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 259.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.7%, Prefix cache hit rate: 87.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:54 [loggers.py:259] Engine 000: Avg prompt throughput: 149.3 tokens/s, Avg generation throughput: 323.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.4%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:43:59 [loggers.py:259] Engine 000: Avg prompt throughput: 9.8 tokens/s, Avg generation throughput: 319.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.5%, Prefix cache hit rate: 87.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:44:04 [loggers.py:259] Engine 000: Avg prompt throughput: 380.0 tokens/s, Avg generation throughput: 352.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.6%, Prefix cache hit rate: 87.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:44:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 387.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.1%, Prefix cache hit rate: 87.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:44:14 [loggers.py:259] Engine 000: Avg prompt throughput: 69.1 tokens/s, Avg generation throughput: 407.2 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.4%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580615, ip=10.128.16.37) INFO 06-08 15:44:19 [loggers.py:259] Engine 000: Avg prompt throughput: 9.0 tokens/s, Avg generation throughput: 432.7 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.2%, Prefix cache hit rate: 87.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:44:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 226.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.9%, Prefix cache hit rate: 88.7% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:44:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 226.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.2%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:44:34 [loggers.py:259] Engine 000: Avg prompt throughput: 17.9 tokens/s, Avg generation throughput: 199.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.0%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:44:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 226.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.3%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 35x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:44:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 226.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.6%, Prefix cache hit rate: 88.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:44:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 432.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.2%, Prefix cache hit rate: 88.5% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:44:53] OK: 483 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:44:53] OK: RSS 1.85 GiB | node mem 253.4/858.0 GiB used (29.5%), avail 604.6 GiB +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:44:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 430.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.8%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 431.3 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.4%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 430.8 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.0%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 388.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.6%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.2%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 383.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.7%, Prefix cache hit rate: 88.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:45:24] OK: 427 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:45:24] OK: RSS 1.44 GiB | node mem 398.2/858.0 GiB used (46.4%), avail 459.7 GiB +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 342.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.8%, Prefix cache hit rate: 88.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:30 [loggers.py:259] Engine 000: Avg prompt throughput: 91.8 tokens/s, Avg generation throughput: 384.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.3%, Prefix cache hit rate: 88.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:45:33] OK: 382 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:45:33] OK: RSS 1.42 GiB | node mem 387.4/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) INFO 06-08 15:45:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 384.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.9%, Prefix cache hit rate: 88.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:45:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 502.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.6%, Prefix cache hit rate: 86.0% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:45:40] OK: 371 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:45:40] OK: RSS 1.36 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:45:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 494.1 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.3%, Prefix cache hit rate: 86.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:45:47] OK: 399 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:45:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:45:50 [loggers.py:259] Engine 000: Avg prompt throughput: 174.0 tokens/s, Avg generation throughput: 505.2 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.8%, Prefix cache hit rate: 85.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:45:55 [loggers.py:259] Engine 000: Avg prompt throughput: 326.9 tokens/s, Avg generation throughput: 572.7 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.7%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:46:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 580.1 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.5%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:46:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 580.9 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.3%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:46:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 552.5 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.0%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:46:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 537.5 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.7%, Prefix cache hit rate: 85.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.4%, Prefix cache hit rate: 88.2% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.6%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.8%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:35 [loggers.py:259] Engine 000: Avg prompt throughput: 10.0 tokens/s, Avg generation throughput: 162.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.1%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:40 [loggers.py:259] Engine 000: Avg prompt throughput: 97.8 tokens/s, Avg generation throughput: 202.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.0%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 228.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.3%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 229.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.6%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:46:53] OK: 510 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:46:53] OK: RSS 1.85 GiB | node mem 253.4/858.0 GiB used (29.5%), avail 604.6 GiB +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:46:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 193.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.2%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:47:00 [loggers.py:259] Engine 000: Avg prompt throughput: 96.2 tokens/s, Avg generation throughput: 199.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.1%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:47:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 230.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.4%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 374.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.6%, Prefix cache hit rate: 90.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:15 [loggers.py:259] Engine 000: Avg prompt throughput: 74.1 tokens/s, Avg generation throughput: 412.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.9%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 411.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:47:25] OK: 383 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:47:25] OK: RSS 1.44 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 374.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.2%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.7%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:47:33] OK: 325 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:47:33] OK: RSS 1.42 GiB | node mem 387.4/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.3%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:47:40] OK: 368 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:47:40] OK: RSS 1.36 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.8%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 375.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.3%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:47:47] OK: 443 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:47:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) INFO 06-08 15:47:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 182.5 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.1%, Prefix cache hit rate: 88.2% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:47:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 376.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.4%, Prefix cache hit rate: 90.7% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:48:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 398.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.7%, Prefix cache hit rate: 87.8% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:48:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 398.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.3%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:48:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 397.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.8%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173452, ip=10.128.16.133) INFO 06-08 15:48:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 396.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.3%, Prefix cache hit rate: 87.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578474, ip=10.128.16.36) INFO 06-08 15:48:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 487.8 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.8%, Prefix cache hit rate: 88.2% [repeated 27x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:27 [loggers.py:259] Engine 000: Avg prompt throughput: 227.1 tokens/s, Avg generation throughput: 405.4 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.0%, Prefix cache hit rate: 88.2% [repeated 83x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 423.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.6%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 411.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.3%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 381.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.8%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 382.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.3%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 383.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.9%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:48:53] OK: 523 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:48:53] OK: RSS 1.86 GiB | node mem 253.4/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:48:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 382.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.4%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:49:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 382.0 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.9%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 15:49:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 348.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.2%, Prefix cache hit rate: 90.1% [repeated 43x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 15:49:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 316.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.2%, Prefix cache hit rate: 90.1% [repeated 53x across cluster] +(AsyncVLLMInferenceEngine pid=1293194, ip=10.128.16.38) INFO 06-08 15:49:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 304.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.7%, Prefix cache hit rate: 90.1% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 15:49:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 417.5 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 45.1%, Prefix cache hit rate: 88.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:49:25] OK: 322 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:49:25] OK: RSS 1.44 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:49:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 255.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.8%, Prefix cache hit rate: 89.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 15:49:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 255.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.2%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:49:33] OK: 326 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:49:33] OK: RSS 1.42 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 15:49:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 458.0 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.7%, Prefix cache hit rate: 88.0% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:49:40] OK: 361 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:49:40] OK: RSS 1.36 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 15:49:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 437.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.8%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 15:49:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 412.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.4%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:49:47] OK: 376 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:49:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 15:49:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 413.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 53.0%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:49:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 134.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.2%, Prefix cache hit rate: 90.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.4%, Prefix cache hit rate: 90.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 134.2 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.6%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.8%, Prefix cache hit rate: 90.2% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.0%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.1%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 134.0 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.3%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 133.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.5%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 15:50:38 [loggers.py:259] Engine 000: Avg prompt throughput: 2.2 tokens/s, Avg generation throughput: 149.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 90.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:50:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 498.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.2%, Prefix cache hit rate: 90.7% [repeated 42x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:50:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 362.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.1%, Prefix cache hit rate: 88.7% [repeated 69x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:50:53] OK: 508 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:50:53] OK: RSS 1.86 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:50:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 361.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.6%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 15:51:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 352.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.6%, Prefix cache hit rate: 88.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:51:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.6%, Prefix cache hit rate: 88.1% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:51:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 280.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.0%, Prefix cache hit rate: 88.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:51:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 270.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.4%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:51:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 269.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 29.8%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:51:25] OK: 328 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:51:25] OK: RSS 1.44 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 15:51:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 269.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.1%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1580614, ip=10.128.16.37) INFO 06-08 15:51:30 [loggers.py:259] Engine 000: Avg prompt throughput: 1.6 tokens/s, Avg generation throughput: 469.6 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 57.0%, Prefix cache hit rate: 90.8% [repeated 39x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:51:33] OK: 329 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:51:33] OK: RSS 1.42 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) INFO 06-08 15:51:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.5%, Prefix cache hit rate: 88.2% [repeated 63x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:51:40] OK: 469 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:51:40] OK: RSS 1.37 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:51:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 706.4 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 97.8%, Prefix cache hit rate: 86.5% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:51:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 680.1 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 98.7%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:51:47] OK: 424 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:51:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:51:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 659.1 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.8%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:51:56 [loggers.py:259] Engine 000: Avg prompt throughput: 312.6 tokens/s, Avg generation throughput: 674.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.2%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 686.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.1%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 685.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.1%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 683.7 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.0%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:16 [loggers.py:259] Engine 000: Avg prompt throughput: 2.4 tokens/s, Avg generation throughput: 712.9 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 96.0%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 729.4 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 96.4%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 705.4 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.4%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 653.2 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 85.4%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) ERROR 06-08 15:52:32 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 647.1 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 86.3%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 644.5 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.2%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:46 [loggers.py:259] Engine 000: Avg prompt throughput: 70.1 tokens/s, Avg generation throughput: 672.8 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.2%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 684.5 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 89.2%, Prefix cache hit rate: 86.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:52:53] OK: 507 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:52:53] OK: RSS 1.86 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:52:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 684.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.1%, Prefix cache hit rate: 86.3% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 15:52:58 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:01 [loggers.py:259] Engine 000: Avg prompt throughput: 1.0 tokens/s, Avg generation throughput: 690.7 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.6%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 684.5 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 91.6%, Prefix cache hit rate: 86.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 682.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.5%, Prefix cache hit rate: 86.4% [repeated 32x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 683.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.5%, Prefix cache hit rate: 86.4% [repeated 63x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:21 [loggers.py:259] Engine 000: Avg prompt throughput: 1545.4 tokens/s, Avg generation throughput: 689.4 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 96.6%, Prefix cache hit rate: 85.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:53:25] OK: 326 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:53:25] OK: RSS 1.44 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 789.7 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 97.7%, Prefix cache hit rate: 85.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 785.8 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 98.8%, Prefix cache hit rate: 85.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:53:33] OK: 389 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:53:33] OK: RSS 1.42 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 780.4 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 99.9%, Prefix cache hit rate: 85.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:53:40] OK: 550 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:53:40] OK: RSS 1.37 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 730.0 tokens/s, Running: 19 reqs, Waiting: 3 reqs, GPU KV cache usage: 99.9%, Prefix cache hit rate: 62.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 674.5 tokens/s, Running: 18 reqs, Waiting: 4 reqs, GPU KV cache usage: 99.1%, Prefix cache hit rate: 62.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:53:47] OK: 444 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:53:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:51 [loggers.py:259] Engine 000: Avg prompt throughput: 4798.1 tokens/s, Avg generation throughput: 554.2 tokens/s, Running: 22 reqs, Waiting: 0 reqs, GPU KV cache usage: 99.8%, Prefix cache hit rate: 61.0% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:53:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 771.7 tokens/s, Running: 20 reqs, Waiting: 2 reqs, GPU KV cache usage: 94.9%, Prefix cache hit rate: 61.0% [repeated 43x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 683.6 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.0%, Prefix cache hit rate: 61.0% [repeated 53x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 789.5 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.1%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 773.7 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.5%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 717.5 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.5%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 28x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 28x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 684.4 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.6%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 681.0 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.6%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 655.9 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.0%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 640.9 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.9%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 625.5 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.7%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 621.0 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 96.6%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:51 [loggers.py:259] Engine 000: Avg prompt throughput: 1.4 tokens/s, Avg generation throughput: 628.7 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 97.5%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 15x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:54:53] OK: 437 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:54:53] OK: RSS 1.86 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222696, ip=10.128.16.135) ERROR 06-08 15:54:55 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:54:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 637.1 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 98.4%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 633.0 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 99.3%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 628.6 tokens/s, Running: 16 reqs, Waiting: 1 reqs, GPU KV cache usage: 100.0%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 605.6 tokens/s, Running: 15 reqs, Waiting: 2 reqs, GPU KV cache usage: 94.8%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 577.9 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.9%, Prefix cache hit rate: 61.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:21 [loggers.py:259] Engine 000: Avg prompt throughput: 648.3 tokens/s, Avg generation throughput: 632.8 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.6%, Prefix cache hit rate: 60.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:55:25] OK: 367 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:55:25] OK: RSS 1.44 GiB | node mem 398.2/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.8 tokens/s, Avg generation throughput: 666.9 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 85.8%, Prefix cache hit rate: 61.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:31 [loggers.py:259] Engine 000: Avg prompt throughput: 996.1 tokens/s, Avg generation throughput: 645.6 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.1%, Prefix cache hit rate: 60.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:55:33] OK: 379 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:55:33] OK: RSS 1.43 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 683.8 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 89.0%, Prefix cache hit rate: 60.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:55:40] OK: 742 / 131,072 FDs open (0.6% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:55:40] OK: RSS 1.37 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.6 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 683.7 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.0%, Prefix cache hit rate: 60.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:46 [loggers.py:259] Engine 000: Avg prompt throughput: 703.1 tokens/s, Avg generation throughput: 698.7 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 91.9%, Prefix cache hit rate: 60.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:55:47] OK: 467 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:55:47] OK: RSS 1.35 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 689.7 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 91.9%, Prefix cache hit rate: 60.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:55:56 [loggers.py:259] Engine 000: Avg prompt throughput: 938.5 tokens/s, Avg generation throughput: 666.2 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.1%, Prefix cache hit rate: 60.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:56:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 739.5 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 96.1%, Prefix cache hit rate: 60.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:56:06 [loggers.py:259] Engine 000: Avg prompt throughput: 1176.4 tokens/s, Avg generation throughput: 699.0 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 91.3%, Prefix cache hit rate: 60.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:56:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 722.0 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 82.2%, Prefix cache hit rate: 60.3% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:56:16 [loggers.py:259] Engine 000: Avg prompt throughput: 699.9 tokens/s, Avg generation throughput: 696.7 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 84.3%, Prefix cache hit rate: 60.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:56:21 [loggers.py:259] Engine 000: Avg prompt throughput: 3.8 tokens/s, Avg generation throughput: 751.1 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.3%, Prefix cache hit rate: 60.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) INFO 06-08 15:56:26 [loggers.py:259] Engine 000: Avg prompt throughput: 536.7 tokens/s, Avg generation throughput: 764.8 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.0%, Prefix cache hit rate: 60.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:56:32 [loggers.py:259] Engine 000: Avg prompt throughput: 1.8 tokens/s, Avg generation throughput: 327.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.0%, Prefix cache hit rate: 91.4% [repeated 42x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:56:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 370.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.6%, Prefix cache hit rate: 91.4% [repeated 55x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:56:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 309.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.2%, Prefix cache hit rate: 91.4% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:56:47 [loggers.py:259] Engine 000: Avg prompt throughput: 3.6 tokens/s, Avg generation throughput: 309.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.4%, Prefix cache hit rate: 91.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) ERROR 06-08 15:56:49 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:56:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 328.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.9%, Prefix cache hit rate: 91.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:56:53] OK: 401 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:56:53] OK: RSS 1.87 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:56:57 [loggers.py:259] Engine 000: Avg prompt throughput: 3.2 tokens/s, Avg generation throughput: 338.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.4%, Prefix cache hit rate: 91.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) ERROR 06-08 15:56:57 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:57:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 373.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.9%, Prefix cache hit rate: 91.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) ERROR 06-08 15:57:06 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:57:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 374.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.4%, Prefix cache hit rate: 91.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:57:08 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:57:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 356.2 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.3%, Prefix cache hit rate: 91.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:57:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 280.0 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.7%, Prefix cache hit rate: 91.5% [repeated 43x across cluster] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) ERROR 06-08 15:57:21 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(AsyncVLLMInferenceEngine pid=1644759, ip=10.128.16.46) INFO 06-08 15:57:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 281.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.1%, Prefix cache hit rate: 91.5% [repeated 54x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:57:25] OK: 397 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:57:25] OK: RSS 1.44 GiB | node mem 398.2/858.0 GiB used (46.4%), avail 459.7 GiB +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 15:57:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.5 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.9%, Prefix cache hit rate: 89.7% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 15:57:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 273.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.3%, Prefix cache hit rate: 89.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:57:33] OK: 698 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:57:33] OK: RSS 1.43 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(AsyncVLLMInferenceEngine pid=1599318, ip=10.128.16.42) INFO 06-08 15:57:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 236.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.6%, Prefix cache hit rate: 89.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:57:40] OK: 1,217 / 131,072 FDs open (0.9% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:57:40] OK: RSS 1.40 GiB | node mem 208.4/858.0 GiB used (24.3%), avail 649.5 GiB +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:57:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.4%, Prefix cache hit rate: 89.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:57:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.6%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:57:47] OK: 459 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:57:47] OK: RSS 1.36 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:57:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.8%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:57:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 138.1 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.0%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.2%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.8 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.3%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.4 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.5%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.7%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.7 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.9%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:58:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 441.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.8%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:58:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 441.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.4%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.5%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.6 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.7%, Prefix cache hit rate: 89.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.5 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.9%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651114, ip=10.128.16.48) INFO 06-08 15:58:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 137.9 tokens/s, Running: 3 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.1%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:58:53] OK: 303 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [15:58:53] OK: RSS 1.87 GiB | node mem 253.3/858.0 GiB used (29.5%), avail 604.6 GiB +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:58:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 440.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.5%, Prefix cache hit rate: 90.0% [repeated 49x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 440.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.1%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 440.6 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.7%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 439.7 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.3%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 410.2 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.8%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 397.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.3%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:59:25] OK: 376 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [15:59:25] OK: RSS 1.44 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:27 [loggers.py:259] Engine 000: Avg prompt throughput: 2.8 tokens/s, Avg generation throughput: 425.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.9%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 440.3 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.5%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:59:33] OK: 656 / 131,072 FDs open (0.5% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [15:59:33] OK: RSS 1.43 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 440.3 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.2%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:59:40] OK: 874 / 131,072 FDs open (0.7% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [15:59:40] OK: RSS 1.41 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.4 GiB +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 441.4 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.8%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 440.3 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 45.4%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:59:47] OK: 465 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [15:59:47] OK: RSS 1.36 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) ERROR 06-08 15:59:48 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:52 [loggers.py:259] Engine 000: Avg prompt throughput: 1.0 tokens/s, Avg generation throughput: 453.2 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 46.8%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] Error in preprocessing prompt inputs [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] Traceback (most recent call last): [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] return await asyncio.gather( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] raise VLLMValidationError( [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 15:59:49 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 15:59:57 [loggers.py:259] Engine 000: Avg prompt throughput: 2.0 tokens/s, Avg generation throughput: 519.5 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 47.6%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1580469, ip=10.128.16.37) ERROR 06-08 15:59:58 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 16:00:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 528.7 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 48.3%, Prefix cache hit rate: 90.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 16:00:07 [loggers.py:259] Engine 000: Avg prompt throughput: 92.9 tokens/s, Avg generation throughput: 538.3 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.3%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 16:00:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 572.4 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.1%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 16:00:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 570.1 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.9%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 16:00:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 570.0 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 53.7%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) INFO 06-08 16:00:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 570.0 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.5%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:00:31 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:00:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 607.6 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 78.9%, Prefix cache hit rate: 84.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:00:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 609.6 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 79.7%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:00:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 589.4 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 80.3%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:00:48 [loggers.py:259] Engine 000: Avg prompt throughput: 9.4 tokens/s, Avg generation throughput: 573.3 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 81.2%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) ERROR 06-08 16:00:52 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:00:53 [loggers.py:259] Engine 000: Avg prompt throughput: 59.0 tokens/s, Avg generation throughput: 613.4 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 82.3%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:00:53] OK: 289 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:00:53] OK: RSS 1.87 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:00:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 629.9 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 83.2%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:03 [loggers.py:259] Engine 000: Avg prompt throughput: 117.3 tokens/s, Avg generation throughput: 604.9 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 83.6%, Prefix cache hit rate: 84.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:08 [loggers.py:259] Engine 000: Avg prompt throughput: 53.6 tokens/s, Avg generation throughput: 602.1 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 84.9%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:13 [loggers.py:259] Engine 000: Avg prompt throughput: 61.3 tokens/s, Avg generation throughput: 615.3 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 86.0%, Prefix cache hit rate: 84.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:18 [loggers.py:259] Engine 000: Avg prompt throughput: 1.4 tokens/s, Avg generation throughput: 620.0 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 86.4%, Prefix cache hit rate: 84.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:23 [loggers.py:259] Engine 000: Avg prompt throughput: 76.3 tokens/s, Avg generation throughput: 649.0 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.5%, Prefix cache hit rate: 84.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:01:25] OK: 423 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:01:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:28 [loggers.py:259] Engine 000: Avg prompt throughput: 84.5 tokens/s, Avg generation throughput: 646.5 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.9%, Prefix cache hit rate: 84.9% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 631.1 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.5%, Prefix cache hit rate: 84.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:01:33] OK: 280 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:01:33] OK: RSS 1.43 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645245, ip=10.128.16.46) INFO 06-08 16:01:38 [loggers.py:259] Engine 000: Avg prompt throughput: 44.6 tokens/s, Avg generation throughput: 637.0 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.8%, Prefix cache hit rate: 85.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:01:40] OK: 305 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:01:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.4 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645244, ip=10.128.16.46) INFO 06-08 16:01:43 [loggers.py:259] Engine 000: Avg prompt throughput: 17.6 tokens/s, Avg generation throughput: 430.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.3%, Prefix cache hit rate: 89.3% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:01:47] OK: 288 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:01:47] OK: RSS 1.37 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 16:01:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 337.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.6%, Prefix cache hit rate: 88.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 16:01:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 327.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 16:01:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 268.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.9%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1599319, ip=10.128.16.42) INFO 06-08 16:02:03 [loggers.py:259] Engine 000: Avg prompt throughput: 13.0 tokens/s, Avg generation throughput: 274.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 16:02:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 311.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.2%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 16:02:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 312.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.7%, Prefix cache hit rate: 86.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 16:02:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 312.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.1%, Prefix cache hit rate: 86.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 16:02:23 [loggers.py:259] Engine 000: Avg prompt throughput: 98.2 tokens/s, Avg generation throughput: 348.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.4%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569537, ip=10.128.16.41) INFO 06-08 16:02:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 356.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.9%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:02:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.2%, Prefix cache hit rate: 88.6% [repeated 43x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:02:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 243.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.6%, Prefix cache hit rate: 88.6% [repeated 54x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 27x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:02:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.9%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:02:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:02:53] OK: 267 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:02:53] OK: RSS 1.87 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.1 GiB +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:02:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.6%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:02:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 243.9 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.9%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:03:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 244.5 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.3%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:03:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 232.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.7%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 38x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:03:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.9%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:03:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 194.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.2%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:03:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 194.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.5%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:03:25] OK: 363 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:03:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:03:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 194.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.7%, Prefix cache hit rate: 88.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:03:33] OK: 262 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:03:33] OK: RSS 1.43 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:03:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 421.2 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 52.9%, Prefix cache hit rate: 89.1% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:03:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 412.9 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.5%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:03:40] OK: 301 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:03:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.3 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:03:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 380.3 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 45.0%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:03:48] OK: 340 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:03:48] OK: RSS 1.37 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:03:48 [loggers.py:259] Engine 000: Avg prompt throughput: 7.6 tokens/s, Avg generation throughput: 421.2 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 45.7%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:03:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 423.1 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 46.3%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:03:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 422.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 46.9%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 44x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:04:03 [loggers.py:259] Engine 000: Avg prompt throughput: 9.8 tokens/s, Avg generation throughput: 438.5 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 47.6%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 41x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651115, ip=10.128.16.48) INFO 06-08 16:04:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 195.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.9%, Prefix cache hit rate: 88.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 34x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:04:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 423.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 48.4%, Prefix cache hit rate: 89.1% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:04:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 423.0 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 49.0%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 45x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:04:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 423.4 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 49.6%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173170, ip=10.128.16.133) INFO 06-08 16:04:29 [loggers.py:259] Engine 000: Avg prompt throughput: 128.0 tokens/s, Avg generation throughput: 435.2 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.8%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 88x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 16:04:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 470.8 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 56.7%, Prefix cache hit rate: 88.3% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 69x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 16:04:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 471.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 57.4%, Prefix cache hit rate: 88.3% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 62x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 16:04:44 [loggers.py:259] Engine 000: Avg prompt throughput: 415.4 tokens/s, Avg generation throughput: 490.3 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 58.8%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 65x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 16:04:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 513.8 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 59.5%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:04:53] OK: 312 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:04:53] OK: RSS 1.87 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:04:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 643.8 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 80.5%, Prefix cache hit rate: 62.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 57x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:04:59 [loggers.py:259] Engine 000: Avg prompt throughput: 1.2 tokens/s, Avg generation throughput: 669.1 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 81.4%, Prefix cache hit rate: 62.8% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 43x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 657.2 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 81.4%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 645.1 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 82.3%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 644.1 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 83.2%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 37x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 644.2 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 84.1%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 61x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 644.2 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 85.0%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:05:25] OK: 208 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:05:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 644.0 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 85.9%, Prefix cache hit rate: 62.8% [repeated 35x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:05:33] OK: 354 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:05:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.4 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 645.0 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 86.8%, Prefix cache hit rate: 62.8% [repeated 61x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 645.4 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 87.7%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:05:40] OK: 411 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:05:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.3 GiB +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 645.1 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 88.6%, Prefix cache hit rate: 62.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:05:48] OK: 432 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:05:48] OK: RSS 1.37 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1578125, ip=10.128.16.36) INFO 06-08 16:05:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 426.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 48.2%, Prefix cache hit rate: 88.1% [repeated 47x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 78x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:54 [loggers.py:259] Engine 000: Avg prompt throughput: 9.8 tokens/s, Avg generation throughput: 661.0 tokens/s, Running: 19 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.4%, Prefix cache hit rate: 62.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:05:59 [loggers.py:259] Engine 000: Avg prompt throughput: 194.9 tokens/s, Avg generation throughput: 690.5 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 92.6%, Prefix cache hit rate: 63.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 711.2 tokens/s, Running: 20 reqs, Waiting: 0 reqs, GPU KV cache usage: 93.6%, Prefix cache hit rate: 63.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 14x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:09 [loggers.py:259] Engine 000: Avg prompt throughput: 9.0 tokens/s, Avg generation throughput: 722.2 tokens/s, Running: 21 reqs, Waiting: 0 reqs, GPU KV cache usage: 94.6%, Prefix cache hit rate: 63.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 46x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:14 [loggers.py:259] Engine 000: Avg prompt throughput: 21.9 tokens/s, Avg generation throughput: 802.4 tokens/s, Running: 23 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.8%, Prefix cache hit rate: 63.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 39x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:19 [loggers.py:259] Engine 000: Avg prompt throughput: 67.4 tokens/s, Avg generation throughput: 844.0 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 97.0%, Prefix cache hit rate: 63.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 39x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 852.7 tokens/s, Running: 24 reqs, Waiting: 1 reqs, GPU KV cache usage: 98.2%, Prefix cache hit rate: 63.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:29 [loggers.py:259] Engine 000: Avg prompt throughput: 9.2 tokens/s, Avg generation throughput: 847.9 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 90.6%, Prefix cache hit rate: 63.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 22x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 849.7 tokens/s, Running: 24 reqs, Waiting: 2 reqs, GPU KV cache usage: 91.8%, Prefix cache hit rate: 63.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 24x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 849.5 tokens/s, Running: 24 reqs, Waiting: 2 reqs, GPU KV cache usage: 93.0%, Prefix cache hit rate: 63.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:44 [loggers.py:259] Engine 000: Avg prompt throughput: 9.6 tokens/s, Avg generation throughput: 847.9 tokens/s, Running: 24 reqs, Waiting: 2 reqs, GPU KV cache usage: 92.5%, Prefix cache hit rate: 63.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 16:06:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 339.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.0%, Prefix cache hit rate: 89.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 31x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:06:53] OK: 491 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:06:53] OK: RSS 1.87 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287378, ip=10.128.16.43) INFO 06-08 16:06:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 338.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 22.5%, Prefix cache hit rate: 89.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(AsyncVLLMInferenceEngine pid=1287376, ip=10.128.16.43) INFO 06-08 16:06:59 [loggers.py:259] Engine 000: Avg prompt throughput: 18.9 tokens/s, Avg generation throughput: 845.6 tokens/s, Running: 24 reqs, Waiting: 0 reqs, GPU KV cache usage: 95.0%, Prefix cache hit rate: 63.4% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 23x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.3%, Prefix cache hit rate: 88.1% [repeated 50x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 20x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 289.8 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:19 [loggers.py:259] Engine 000: Avg prompt throughput: 149.2 tokens/s, Avg generation throughput: 266.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 255.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.1%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:07:25] OK: 338 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:07:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:29 [loggers.py:259] Engine 000: Avg prompt throughput: 8.2 tokens/s, Avg generation throughput: 296.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.6%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:07:33] OK: 413 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:07:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.4 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 281.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.4%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 255.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:07:40] OK: 360 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:07:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.3 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 28x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 254.6 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.1%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:07:48] OK: 426 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:07:48] OK: RSS 1.38 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 42x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:49 [loggers.py:259] Engine 000: Avg prompt throughput: 337.1 tokens/s, Avg generation throughput: 327.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.1%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 314.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 14.8%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:07:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 294.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.2%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 294.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.7%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 16x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.1%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.5%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 26x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.9%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 25x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 294.6 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.3%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:30 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 294.8 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.7%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.1%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.5%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:45 [loggers.py:259] Engine 000: Avg prompt throughput: 42.8 tokens/s, Avg generation throughput: 308.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 337.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.2%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:08:53] OK: 516 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:08:53] OK: RSS 1.87 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:08:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 337.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.7%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:09:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 337.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.1%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:09:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 299.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 20.6%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:09:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.0%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1651113, ip=10.128.16.48) INFO 06-08 16:09:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 295.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.4%, Prefix cache hit rate: 88.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 16:09:20 [loggers.py:259] Engine 000: Avg prompt throughput: 211.5 tokens/s, Avg generation throughput: 659.0 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.3%, Prefix cache hit rate: 88.0% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:09:25] OK: 384 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:09:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 16:09:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 600.1 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.6%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 16:09:30 [loggers.py:259] Engine 000: Avg prompt throughput: 101.0 tokens/s, Avg generation throughput: 579.7 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.8%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:09:33] OK: 358 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:09:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.4 GiB +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 16:09:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 569.9 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.0%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599107, ip=10.128.16.42) INFO 06-08 16:09:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 548.2 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.8%, Prefix cache hit rate: 88.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:09:40] OK: 381 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:09:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.3 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 16:09:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 397.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.1%, Prefix cache hit rate: 90.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:09:48] OK: 423 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:09:48] OK: RSS 1.38 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 16:09:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 373.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.6%, Prefix cache hit rate: 90.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1578319, ip=10.128.16.36) INFO 06-08 16:09:55 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 374.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.1%, Prefix cache hit rate: 90.5% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 16:10:00 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 342.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.0%, Prefix cache hit rate: 89.4% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 16:10:05 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 342.3 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.5%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 16:10:10 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 342.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.9%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 16:10:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 341.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.4%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 16:10:20 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 340.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.9%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) INFO 06-08 16:10:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 341.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.4%, Prefix cache hit rate: 89.4% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 16:10:30 [loggers.py:259] Engine 000: Avg prompt throughput: 59.2 tokens/s, Avg generation throughput: 247.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.6%, Prefix cache hit rate: 91.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 16:10:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 212.6 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.6%, Prefix cache hit rate: 91.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 16:10:40 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 15.8%, Prefix cache hit rate: 91.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 16:10:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.1%, Prefix cache hit rate: 91.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1599317, ip=10.128.16.42) INFO 06-08 16:10:50 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 16.3%, Prefix cache hit rate: 91.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:10:53] OK: 517 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:10:53] OK: RSS 1.88 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:10:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 415.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 45.3%, Prefix cache hit rate: 90.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:01 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 327.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.1%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 287.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.5%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 250.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.9%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 239.6 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.2%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:21 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 239.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.5%, Prefix cache hit rate: 90.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:11:25] OK: 341 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:11:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:26 [loggers.py:259] Engine 000: Avg prompt throughput: 83.3 tokens/s, Avg generation throughput: 241.3 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.0%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 288.7 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.4%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:11:33] OK: 328 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:11:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.4 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287377, ip=10.128.16.43) INFO 06-08 16:11:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 292.4 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 30.8%, Prefix cache hit rate: 90.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:11:40] OK: 364 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:11:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.3 GiB +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 16:11:41 [loggers.py:259] Engine 000: Avg prompt throughput: 57.3 tokens/s, Avg generation throughput: 441.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.4%, Prefix cache hit rate: 88.2% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) INFO 06-08 16:11:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 423.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.4%, Prefix cache hit rate: 88.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:11:48] OK: 355 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:11:48] OK: RSS 1.38 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 16:11:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 200.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.6%, Prefix cache hit rate: 89.7% [repeated 46x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 16:11:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 170.1 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 17.8%, Prefix cache hit rate: 89.7% [repeated 52x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 16:12:01 [loggers.py:259] Engine 000: Avg prompt throughput: 32.7 tokens/s, Avg generation throughput: 183.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 19.2%, Prefix cache hit rate: 89.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222484, ip=10.128.16.135) INFO 06-08 16:12:06 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 206.8 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 18.3%, Prefix cache hit rate: 89.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:11 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 460.8 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.2%, Prefix cache hit rate: 89.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:16 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 460.2 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 44.8%, Prefix cache hit rate: 89.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:21 [loggers.py:259] Engine 000: Avg prompt throughput: 204.0 tokens/s, Avg generation throughput: 471.9 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 46.4%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 21x across cluster] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:26 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 502.0 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 47.1%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:31 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 460.8 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.8%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:36 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 458.7 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.5%, Prefix cache hit rate: 89.6% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:41 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 458.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.1%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:46 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 458.8 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.8%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:51 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 459.4 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.4%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:12:53] OK: 517 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:12:53] OK: RSS 1.88 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1292985, ip=10.128.16.38) INFO 06-08 16:12:56 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 459.0 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.0%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:02 [loggers.py:259] Engine 000: Avg prompt throughput: 57.9 tokens/s, Avg generation throughput: 286.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.8%, Prefix cache hit rate: 89.1% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:07 [loggers.py:259] Engine 000: Avg prompt throughput: 1.8 tokens/s, Avg generation throughput: 350.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.4%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:12 [loggers.py:259] Engine 000: Avg prompt throughput: 117.6 tokens/s, Avg generation throughput: 392.4 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.3%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 377.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.4%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 318.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.8%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:13:25] OK: 342 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:13:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.3%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 317.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.7%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:13:33] OK: 334 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:13:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 318.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.2%, Prefix cache hit rate: 89.0% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:13:40] OK: 362 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:13:40] OK: RSS 1.43 GiB | node mem 208.6/858.0 GiB used (24.3%), avail 649.3 GiB +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:42 [loggers.py:259] Engine 000: Avg prompt throughput: 143.1 tokens/s, Avg generation throughput: 318.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.7%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 362.6 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.2%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:13:48] OK: 355 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:13:48] OK: RSS 1.38 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 364.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.7%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:13:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 358.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:14:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 318.0 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.4%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=2173451, ip=10.128.16.133) ERROR 06-08 16:14:06 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:14:07 [loggers.py:259] Engine 000: Avg prompt throughput: 3.2 tokens/s, Avg generation throughput: 333.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.0%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:14:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 363.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.5%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:14:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 362.7 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.0%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) INFO 06-08 16:14:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 361.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 37.5%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 366.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.0%, Prefix cache hit rate: 90.7% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 366.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.5%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:14:32 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 324.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.3%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.7%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.2%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.6%, Prefix cache hit rate: 90.7% [repeated 44x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:14:53] OK: 516 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:14:53] OK: RSS 1.88 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:14:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.3 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.1%, Prefix cache hit rate: 90.7% [repeated 53x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:15:02 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.5 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.5%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1650837, ip=10.128.16.48) ERROR 06-08 16:15:04 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:15:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 322.4 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.0%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) INFO 06-08 16:15:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 321.2 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 43.4%, Prefix cache hit rate: 90.7% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:17 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 350.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 32.9%, Prefix cache hit rate: 89.1% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:22 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 351.0 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.4%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:15:25] OK: 322 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:15:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 350.2 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.9%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 348.8 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.4%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:15:33] OK: 322 / 131,072 FDs open (0.2% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:15:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 350.4 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.9%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:15:40] OK: 338 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:15:40] OK: RSS 1.43 GiB | node mem 208.7/858.0 GiB used (24.3%), avail 649.3 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 331.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.6%, Prefix cache hit rate: 89.1% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:47 [loggers.py:259] Engine 000: Avg prompt throughput: 20.4 tokens/s, Avg generation throughput: 346.9 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 36.2%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:15:48] OK: 341 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:15:48] OK: RSS 1.38 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:52 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 315.1 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 33.9%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:53 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:15:57 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 262.9 tokens/s, Running: 6 reqs, Waiting: 0 reqs, GPU KV cache usage: 34.3%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=2173450, ip=10.128.16.133) ERROR 06-08 16:15:53 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:16:02 [loggers.py:259] Engine 000: Avg prompt throughput: 68.4 tokens/s, Avg generation throughput: 281.1 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 35.4%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438753, ip=10.128.16.45) ERROR 06-08 16:15:59 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) INFO 06-08 16:16:07 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 424.0 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.3%, Prefix cache hit rate: 89.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 16:16:12 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 612.4 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 60.0%, Prefix cache hit rate: 83.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 16:16:17 [loggers.py:259] Engine 000: Avg prompt throughput: 1.8 tokens/s, Avg generation throughput: 591.3 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 61.0%, Prefix cache hit rate: 83.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 10x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 16:16:22 [loggers.py:259] Engine 000: Avg prompt throughput: 69.0 tokens/s, Avg generation throughput: 625.9 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 62.4%, Prefix cache hit rate: 83.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 6x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 16:16:27 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 660.9 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 63.4%, Prefix cache hit rate: 83.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1292984, ip=10.128.16.38) ERROR 06-08 16:16:32 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 16:16:32 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 667.3 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 64.3%, Prefix cache hit rate: 83.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] Error in preprocessing prompt inputs [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] Traceback (most recent call last): [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 6x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] return await asyncio.gather( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] tokens = validator(tokenizer, tokens) [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] raise VLLMValidationError( [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1287233, ip=10.128.16.43) ERROR 06-08 16:16:36 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:16:37 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 305.9 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.3%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 9x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 9x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 15x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:16:42 [loggers.py:259] Engine 000: Avg prompt throughput: 0.6 tokens/s, Avg generation throughput: 340.5 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 38.8%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:16:47 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 349.1 tokens/s, Running: 8 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.3%, Prefix cache hit rate: 89.2% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 44x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1292851, ip=10.128.16.38) INFO 06-08 16:16:52 [loggers.py:259] Engine 000: Avg prompt throughput: 165.2 tokens/s, Avg generation throughput: 396.9 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 42.1%, Prefix cache hit rate: 89.2% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:16:53] OK: 515 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:16:53] OK: RSS 1.88 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 29x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:16:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 608.5 tokens/s, Running: 13 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.3%, Prefix cache hit rate: 89.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 38x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 565.0 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.0%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:08 [loggers.py:259] Engine 000: Avg prompt throughput: 83.6 tokens/s, Avg generation throughput: 610.7 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 51.8%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 8x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 30x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569679, ip=10.128.16.41) INFO 06-08 16:17:13 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 666.9 tokens/s, Running: 15 reqs, Waiting: 0 reqs, GPU KV cache usage: 62.1%, Prefix cache hit rate: 83.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 5x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 19x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:18 [loggers.py:259] Engine 000: Avg prompt throughput: 234.7 tokens/s, Avg generation throughput: 634.5 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 55.7%, Prefix cache hit rate: 89.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) Provider List: https://docs.litellm.ai/docs/providers [repeated 17x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 654.5 tokens/s, Running: 14 reqs, Waiting: 0 reqs, GPU KV cache usage: 56.6%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:17:25] OK: 340 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:17:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 7x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 7x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 18x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:28 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 612.8 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.6%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 4x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 4x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1569678, ip=10.128.16.41) ERROR 06-08 16:17:32 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 561.1 tokens/s, Running: 12 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.4%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:17:33] OK: 336 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:17:33] OK: RSS 1.44 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.4 GiB +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:38 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 553.6 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 40.3%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:17:40] OK: 347 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:17:40] OK: RSS 1.44 GiB | node mem 208.7/858.0 GiB used (24.3%), avail 649.3 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 514.8 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.0%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 513.6 tokens/s, Running: 11 reqs, Waiting: 0 reqs, GPU KV cache usage: 41.7%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:17:48] OK: 338 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:17:48] OK: RSS 1.38 GiB | node mem 429.9/858.0 GiB used (50.1%), avail 428.1 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 12x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 510.8 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.0%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1569680, ip=10.128.16.41) INFO 06-08 16:17:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 468.5 tokens/s, Running: 10 reqs, Waiting: 0 reqs, GPU KV cache usage: 39.7%, Prefix cache hit rate: 89.5% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1580613, ip=10.128.16.37) ERROR 06-08 16:18:02 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:03 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 701.0 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 67.3%, Prefix cache hit rate: 86.5% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 13x across cluster] +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 694.9 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 68.3%, Prefix cache hit rate: 86.5% [repeated 50x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:13 [loggers.py:259] Engine 000: Avg prompt throughput: 45.6 tokens/s, Avg generation throughput: 668.9 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 70.7%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 673.2 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 71.6%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 10x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:23 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 659.5 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 71.7%, Prefix cache hit rate: 86.6% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:28 [loggers.py:259] Engine 000: Avg prompt throughput: 21.2 tokens/s, Avg generation throughput: 655.0 tokens/s, Running: 18 reqs, Waiting: 0 reqs, GPU KV cache usage: 76.0%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:33 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 673.0 tokens/s, Running: 16 reqs, Waiting: 0 reqs, GPU KV cache usage: 67.1%, Prefix cache hit rate: 86.7% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(AsyncVLLMInferenceEngine pid=3222698, ip=10.128.16.135) INFO 06-08 16:18:38 [loggers.py:259] Engine 000: Avg prompt throughput: 105.1 tokens/s, Avg generation throughput: 661.8 tokens/s, Running: 17 reqs, Waiting: 0 reqs, GPU KV cache usage: 69.0%, Prefix cache hit rate: 86.8% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 16:18:43 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 405.6 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 48.8%, Prefix cache hit rate: 89.6% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 16:18:48 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.3 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 49.4%, Prefix cache hit rate: 89.6% [repeated 48x across cluster] +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:18:53] OK: 496 / 131,072 FDs open (0.4% of soft limit, hard limit: 131,072) +(skyrl_entrypoint pid=1755734) [fd-monitor] [16:18:53] OK: RSS 1.89 GiB | node mem 252.8/858.0 GiB used (29.5%), avail 605.2 GiB +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:18:53 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.0%, Prefix cache hit rate: 89.8% [repeated 49x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 16:18:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 404.7 tokens/s, Running: 9 reqs, Waiting: 0 reqs, GPU KV cache usage: 50.5%, Prefix cache hit rate: 89.6% [repeated 47x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:04 [loggers.py:259] Engine 000: Avg prompt throughput: 132.4 tokens/s, Avg generation throughput: 204.1 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.4%, Prefix cache hit rate: 89.9% [repeated 49x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:09 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.3 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.7%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:14 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.7 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.1%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:19 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.2 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.4%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 7x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] Error in preprocessing prompt inputs +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] Traceback (most recent call last): +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 299, in render_chat_request +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] conversation, engine_prompts = await self._preprocess_chat( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/entrypoints/openai/engine/serving.py", line 1018, in _preprocess_chat +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] (conversation,), (engine_prompt,) = await renderer.render_chat_async( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 381, in render_chat_async +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 278, in tokenize_prompts_async +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] return await asyncio.gather( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/protocol.py", line 271, in tokenize_prompt_async +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] return params.apply_post_tokenization(self.tokenizer, prompt) # type: ignore[arg-type] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 373, in apply_post_tokenization +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] prompt["prompt_token_ids"] = self._validate_tokens( # type: ignore[typeddict-unknown-key] +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 357, in _validate_tokens +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] tokens = validator(tokenizer, tokens) +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/vllm/renderers/params.py", line 337, in _token_len_check +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] raise VLLMValidationError( +(AsyncVLLMInferenceEngine pid=1645243, ip=10.128.16.46) ERROR 06-08 16:19:19 [serving.py:315] vllm.exceptions.VLLMValidationError: You passed 32769 input tokens and requested 0 output tokens. However, the model's context length is only 32768 tokens, resulting in a maximum input length of 32768 tokens. Please reduce the length of the input prompt. (parameter=input_tokens, value=32769) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 3x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 3x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:24 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 224.4 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 26.7%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 8x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:19:25] OK: 341 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) [fd-monitor] [16:19:25] OK: RSS 1.45 GiB | node mem 398.3/858.0 GiB used (46.4%), avail 459.7 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:29 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 225.0 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 27.0%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 6x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 2x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:19:33] OK: 329 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) [fd-monitor] [16:19:33] OK: RSS 1.45 GiB | node mem 387.5/858.0 GiB used (45.2%), avail 470.5 GiB +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:34 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 207.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.7%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 2x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:39 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.9 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.9%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:19:40] OK: 329 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) [fd-monitor] [16:19:40] OK: RSS 1.44 GiB | node mem 208.7/858.0 GiB used (24.3%), avail 649.2 GiB +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:44 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 179.7 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.2%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:19:48] OK: 338 / 131,072 FDs open (0.3% of soft limit, hard limit: 131,072) +(RolloutCoordinator pid=1579300, ip=10.128.16.36) [fd-monitor] [16:19:48] OK: RSS 1.38 GiB | node mem 430.0/858.0 GiB used (50.1%), avail 428.0 GiB +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:49 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.4%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:54 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.0 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.7%, Prefix cache hit rate: 89.9% [repeated 48x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new +(RolloutCoordinator pid=3223698, ip=10.128.16.135) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) Provider List: https://docs.litellm.ai/docs/providers [repeated 11x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 55x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 55x across cluster] +(AsyncVLLMInferenceEngine pid=1438458, ip=10.128.16.45) INFO 06-08 16:19:59 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 180.2 tokens/s, Running: 4 reqs, Waiting: 0 reqs, GPU KV cache usage: 25.9%, Prefix cache hit rate: 89.9% [repeated 44x across cluster] +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=3223698, ip=10.128.16.135) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Provider List: https://docs.litellm.ai/docs/providers [repeated 5x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1570740, ip=10.128.16.41) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(RolloutCoordinator pid=1529797, ip=10.128.16.44) +(raylet) A worker died or was killed while executing a task by an unexpected system error. To troubleshoot the problem, check the logs for the dead worker. Lease ID: 0000000002000000ffffffffffffffffffffffffffffffffffffffffffffffff Worker ID: b564da496b5575693dee4e5fbea556936f07a3c25484bb856edd7f0e Node ID: 4707e56aaefa98735573d7083596814077f802cefd41d415b6eecd08 Worker IP address: 10.128.16.35 Worker port: 10292 Worker PID: 1755734 Worker exit type: SYSTEM_ERROR Worker exit detail: The leased worker has unrecoverable failure. Worker is requested to be destroyed when it is returned. Worker exits with an exit code 1. +(RolloutCoordinator pid=1529797, ip=10.128.16.44) Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new [repeated 133x across cluster] +(RolloutCoordinator pid=1529797, ip=10.128.16.44) LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'. [repeated 133x across cluster] +(AsyncVLLMInferenceEngine pid=1621492, ip=10.128.16.47) INFO 06-08 16:20:04 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 300.7 tokens/s, Running: 7 reqs, Waiting: 0 reqs, GPU KV cache usage: 21.9%, Prefix cache hit rate: 89.6% [repeated 43x across cluster] +Error executing job with overrides: ['+terminal_bench_config=terminal_bench', 'trainer.strategy=fsdp2', 'trainer.algorithm.advantage_estimator=rloo_n', 'trainer.algorithm.use_kl_loss=false', 'trainer.algorithm.kl_loss_coef=0.0', 'trainer.algorithm.eps_clip_low=0.2', 'trainer.algorithm.eps_clip_high=0.05', 'trainer.algorithm.loss_reduction=sequence_mean', 'trainer.epochs=2', 'trainer.max_steps=80', 'trainer.update_epochs_per_batch=1', 'trainer.train_batch_size=64', 'trainer.policy_mini_batch_size=64', 'trainer.eval_batch_size=64', 'trainer.micro_forward_batch_size_per_gpu=4', 'trainer.micro_train_batch_size_per_gpu=1', 'trainer.max_prompt_length=999999', 'trainer.eval_interval=999999', 'trainer.eval_before_train=false', 'trainer.ckpt_interval=2', 'trainer.resume_mode=latest', 'trainer.hf_save_interval=5', '++trainer.hf_hub_repo_id=laion/ablation-pymethods2test-seqmean-arm0', '++trainer.hf_hub_private=false', '++trainer.hf_hub_revision=main', '++trainer.enable_db_registration=false', 'trainer.project_name=OpenThoughts-Agent', 'trainer.log_level=INFO', 'trainer.tracker_commit_each_step=true', 'trainer.logger=console', 'trainer.run_name=ablation-pymethods2test-seqmean-arm0', 'trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/checkpoints', 'trainer.export_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/exports', 'trainer.policy.optimizer_config.lr=8e-6', 'trainer.policy.optimizer_config.weight_decay=0.0', 'trainer.policy.optimizer_config.adam_betas=[0.9,0.999]', 'trainer.policy.optimizer_config.max_grad_norm=0.9', 'trainer.policy.fsdp_config.cpu_offload=false', 'trainer.policy.fsdp_config.reshard_after_forward=true', 'trainer.policy.fsdp_config.fsdp_size=4', 'trainer.policy.model.path=/e/home/jusers/feuer1/jupiter/.cache/huggingface/hub/models--laion--GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink/snapshots/0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'trainer.ref.fsdp_config.cpu_offload=false', 'trainer.ref.fsdp_config.reshard_after_forward=true', 'trainer.ref.fsdp_config.fsdp_size=4', 'trainer.placement.colocate_all=false', 'trainer.placement.policy_num_nodes=2', 'trainer.placement.ref_num_nodes=2', 'trainer.placement.policy_num_gpus_per_node=4', 'trainer.placement.ref_num_gpus_per_node=4', 'trainer.fully_async.max_staleness_steps=16', 'trainer.fully_async.num_parallel_generation_workers=338', 'generator.backend=vllm', 'generator.timeout_multiplier=1.0', 'generator.model_dtype=bfloat16', 'generator.inference_engine_tensor_parallel_size=1', 'generator.num_inference_engines=48', 'generator.n_samples_per_prompt=8', 'generator.eval_n_samples_per_prompt=8', 'generator.gpu_memory_utilization=0.75', 'generator.max_num_seqs=24', 'generator.max_num_batched_tokens=65536', 'generator.enable_prefix_caching=true', 'generator.enable_chunked_prefill=true', 'generator.run_engines_locally=true', 'generator.weight_sync_backend=nccl', 'generator.async_engine=true', 'generator.batched=false', 'generator.enable_http_endpoint=true', 'generator.enable_ray_prometheus_stats=false', 'generator.vllm_stats_interval=1', 'generator.append_eos_token_after_stop_str_in_multi_turn=true', 'generator.max_turns=999999', 'generator.sampling_params.max_generate_length=4096', 'generator.sampling_params.temperature=0.7', 'generator.sampling_params.top_p=0.95', 'generator.sampling_params.top_k=20', '++generator.engine_init_kwargs.max_model_len=32768', '++generator.engine_init_kwargs.custom_chat_template_chat_completion_path=chat_templates/qwen3_thinking_acc.jinja2', '++generator.engine_init_kwargs.served_model_name=0e3bff0c4e51f6b9ec0713b98b9eec36efb91cc6', 'data.train_data=["/e/scratch/jureap59/feuer1/tasks/exp_rpt_pymethods2test-large"]', 'data.val_data=[]', '+terminal_bench_config.trials_dir=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/trace_jobs', '+terminal_bench_config.harbor.name=terminus-2', '+terminal_bench_config.harbor.max_episodes=999999', '+terminal_bench_config.harbor.enable_summarize=false', '+terminal_bench_config.harbor.store_all_messages=true', '+terminal_bench_config.harbor.trajectory_config.raw_content=true', '+terminal_bench_config.harbor.enable_episode_logging=false', '+terminal_bench_config.harbor.record_terminal_session=false', '+terminal_bench_config.harbor.enable_pane_logging=false', '+terminal_bench_config.harbor.strict_json_parser=true', '+terminal_bench_config.harbor.interleaved_thinking=true', '+terminal_bench_config.harbor.extra_body.chat_template_kwargs.enable_thinking=true', '+terminal_bench_config.harbor.override_timeout_sec=900', '+terminal_bench_config.harbor.override_cpus=1', '+terminal_bench_config.harbor.override_memory_mb=2048', '+terminal_bench_config.harbor.override_storage_mb=2048', '+terminal_bench_config.harbor.auto_snapshot=true', '+terminal_bench_config.harbor.verifier_override_timeout_sec=120', '+terminal_bench_config.harbor.max_retries=3', '+terminal_bench_config.harbor.min_wait_sec=60.0', '+terminal_bench_config.harbor.max_wait_sec=600.0', '+terminal_bench_config.harbor.wait_multiplier=2.0', '+terminal_bench_config.harbor.exclude_exceptions=["VerifierTimeoutError","VerifierRuntimeError","RewardFileNotFoundError","RewardFileEmptyError","VerifierOutputParseError"]', '+terminal_bench_config.harbor.n_concurrent_trials=675', '+terminal_bench_config.harbor.log_level=INFO', '+terminal_bench_config.harbor.enable_reward_shaping=false', '+terminal_bench_config.harbor.enable_error_classification=true', '+terminal_bench_config.harbor.mask_exceptions=["DaytonaError","EnvironmentStartTimeoutError","NetworkError","ConnectionError","RewardFileNotFoundError","RewardFileEmptyError","AgentEnvironmentTimeoutError","ContextLengthExceededError"]', '+terminal_bench_config.harbor.default_error_treatment=zero', '+terminal_bench_config.harbor.passthrough_exceptions=["AgentTimeoutError"]', '+terminal_bench_config.harbor.zero_exceptions=[]', '+terminal_bench_config.model_info.max_input_tokens=32000', '+terminal_bench_config.model_info.max_output_tokens=4096', '+terminal_bench_config.archiving.enabled=false', '+terminal_bench_config.trace_upload.enabled=true', '+terminal_bench_config.trace_upload.repo_org=DCAgent', '+terminal_bench_config.trace_upload.episodes=last', '+terminal_bench_config.trace_upload.dataset_type=SFT', '+terminal_bench_config.trace_upload.cleanup=true', 'trainer.enable_db_registration=false', 'trainer.ckpt_path=/e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0/ablation-pymethods2test-seqmean-arm0/checkpoints', 'trainer.resume_mode=latest'] +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 142, in + main() + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/main.py", line 94, in decorated_main + _run_hydra( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra + _run_app( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 457, in _run_app + run_and_report( + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 223, in run_and_report + raise ex + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 220, in run_and_report + return func() + ^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/utils.py", line 458, in + lambda: hydra.run( + ^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/_internal/hydra.py", line 132, in run + _ = ret.return_value + ^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 260, in return_value + raise self._return_value + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/hydra/core/utils.py", line 186, in run_job + ret.return_value = task_function(task_cfg) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/SkyRL/skyrl-train/examples/terminal_bench/entrypoints/main_tbench.py", line 132, in main + ray.get(skyrl_entrypoint.remote(cfg)) + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 2967, in get + values, debugger_breakpoint = worker.get_objects( + ^^^^^^^^^^^^^^^^^^^ + File "/e/scratch/jureap59/feuer1/OpenThoughts-Agent/envs/rl/lib/python3.12/site-packages/ray/_private/worker.py", line 1017, in get_objects + raise value +ray.exceptions.WorkerCrashedError: The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. +Stopping Ray cluster... + Warning: Failed to stop Ray on jpbo-001-35: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-35', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds + Warning: Failed to stop Ray on jpbo-001-37: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-37', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds + Warning: Failed to stop Ray on jpbo-001-38: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-38', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds + Warning: Failed to stop Ray on jpbo-001-41: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-41', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds + Warning: Failed to stop Ray on jpbo-001-48: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-001-48', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds + Warning: Failed to stop Ray on jpbo-003-37: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-003-37', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds + Warning: Failed to stop Ray on jpbo-003-39: Command '['srun', '--export=ALL,WANDB_MODE=offline,GLOO_USE_IPV6=0,NCCL_SOCKET_FAMILY=AF_INET,VLLM_FORCE_IPV4=1,VLLM_SKIP_FLAG_DISCOVERY=1,SKYRL_ENABLE_NUMA_AFFINITY=1,DISABLE_AIOHTTP_TRANSPORT=True,VLLM_ALLREDUCE_USE_SYMM_MEM=0,TORCH_CUDNN_SDPA_ENABLED=0,PYTHONFAULTHANDLER=1,TORCH_NCCL_ASYNC_ERROR_HANDLING=1,TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=1800,TORCH_NCCL_BLOCKING_WAIT_TIMEOUT_MS=1800000,VLLM_MQ_MAX_CHUNKS=240,OT_AGENT_RAY_LOG_DIR=/e/data1/datasets/playground/ot-baf/experiments/_ray_logs', '--nodes=1', '--ntasks=1', '--overlap', '--cpu-bind=none', '-w', 'jpbo-003-39', 'bash', '-c', 'unset LD_PRELOAD PROXYCHAINS_CONF_FILE 2>/dev/null; ray stop --force']' timed out after 30 seconds +Ray cluster stopped +[RLJobRunner] Crash detected (exit!=0) — preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0/ray_logs BEFORE trace upload (so a wall-clock kill can't lose crash evidence)... + Collecting Ray logs from worker jpbo-001-36... + Collecting Ray logs from worker jpbo-001-37... + Collecting Ray logs from worker jpbo-001-38... + Collecting Ray logs from worker jpbo-001-41... + Collecting Ray logs from worker jpbo-001-42... + Collecting Ray logs from worker jpbo-001-43... + Collecting Ray logs from worker jpbo-001-44... + Collecting Ray logs from worker jpbo-001-45... + Collecting Ray logs from worker jpbo-001-46... + Collecting Ray logs from worker jpbo-001-47... + Collecting Ray logs from worker jpbo-001-48... + Collecting Ray logs from worker jpbo-003-37... + Collecting Ray logs from worker jpbo-003-39... +[RLJobRunner] Crash-time Ray log preservation complete. +[RLJobRunner] Launching trace upload (training exit code: 1): + repo_id: DCAgent/ablation-pymethods2test-seqmean-arm0 + job_dir: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ablation-pymethods2test-seqmean-arm0 + episodes: last + log: /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/logs/ablation-pymethods2test-seqmean-arm0_trace_upload.log +[RLJobRunner] Waiting for trace upload to complete... +[RLJobRunner] Trace upload failed with exit code 1. +Preserving Ray logs to /e/data1/datasets/playground/ot-baf/ablation-pymethods2test-seqmean-arm0_2/ray_logs/ + Collecting Ray logs from worker jpbo-001-36... + Collecting Ray logs from worker jpbo-001-37... + Collecting Ray logs from worker jpbo-001-38... + Collecting Ray logs from worker jpbo-001-41... + Collecting Ray logs from worker jpbo-001-42... + Collecting Ray logs from worker jpbo-001-43... + Collecting Ray logs from worker jpbo-001-44... + Collecting Ray logs from worker jpbo-001-45... + Collecting Ray logs from worker jpbo-001-46... + Collecting Ray logs from worker jpbo-001-47... + Collecting Ray logs from worker jpbo-001-48... + Collecting Ray logs from worker jpbo-003-37... + Collecting Ray logs from worker jpbo-003-39... +Ray log 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