551 lines
25 KiB
Python
551 lines
25 KiB
Python
"""
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xc_validation_strategy_vllm_zhouyuanxi — 主入口
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启动后针对 3 张 GPU 卡（Kunlunxin_p-800 / Biren_166m / Cambricon_mlu-370-x8）
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分别批量提交各自筛选出的模型适配任务（/api/adapt/task/add，xc-Token 认证）。
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提交账号采用自动 fallback 轮转：优先用 zhouyuanxi 账号提交，一旦该账号命中
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平台的"异步验证任务数量已达上限（100）"限制（错误码 60007），自动切换到下一个
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账号（jiajing → fanyi）继续提交同一个模型，直至全部账号额度用尽。
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之后保持 HTTP 服务存活，暴露 /health（K8s 探活）和 /status（运行状态）。
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"""
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import json
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import os
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import signal
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import threading
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from datetime import datetime
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from typing import List, Tuple
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import requests
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# ══════════════════════════════════════════════════════════
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# 配置
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# ══════════════════════════════════════════════════════════
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BASE_URL = os.environ.get("BASE_URL", "https://modelhub.org.cn")
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ADD_TASK_ENDPOINT = "/api/adapt/task/add"
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TASK_TYPE = "text-generation"
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STRATEGY_ID = os.environ.get("STRATEGY_ID", "") # 平台自动注入，无需修改
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HTTP_HOST = "0.0.0.0"
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HTTP_PORT = 8080
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# 提交账号（按优先级排列，前一个额度满了自动切换到下一个）
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ACCOUNTS: List[Tuple[str, str, str]] = [
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("zhouyuanxi", "i-zhouyuanxi@4paradigm.com", "62b9b487eff2488fb9f1da0b963f0b93"),
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("jiajing", "jiajing", "5e051e0ff8384a81af53bea780deb28a"),
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("fanyi", "fanyi", "f2d501c9ae6543a589cd6cb789108c41"),
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]
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# ══════════════════════════════════════════════════════════
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# 各 GPU 的模型列表（来自 filter_verified_models 脚本的筛选结果）
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# ══════════════════════════════════════════════════════════
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KUNLUNXIN_MODELS = [
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"deepvk/llava-saiga-8b",
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"Intel/llava-gemma-2b",
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"llava-hf/bakLlava-v1-hf",
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"llava-hf/llava-interleave-qwen-0.5b-hf",
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"mistral-experimental/pixtral-12b",
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"fancyfeast/llama-joycaption-beta-one-hf-llava",
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"zhibinlan/UME-R1-2B",
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"osunlp/UGround-V1-2B",
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"Gryphe/Pantheon-RP-1.6-12b-Nemo",
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"Rakuten/RakutenAI-2.0-mini-instruct",
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"OS-Copilot/OS-Atlas-Pro-7B",
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"Dldermann/food_waste",
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"SicariusSicariiStuff/Sweet_Dreams_12B",
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"CYFRAGOVPL/PLLuM-12B-instruct-2412",
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"model-organisms-for-real/kd-student-gemma-olmo-milsub-fd-mixed-alpha-1-nofilter-1samp-5e-5",
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"allenai/OLMo-7B-1024-preview",
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"MrLight/dse-qwen2-2b-mrl-v1",
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"opendatalab/MinerU2.5-2509-1.2B",
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"opendatalab/MinerU2.5-Pro-2605-1.2B",
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"tttt111/mistral-8b-test",
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"Rakuten/RakutenAI-2.0-mini",
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"TheDrummer/UnslopNemo-12B-v4.1",
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"opendatalab/MinerU2.5-Pro-2604-1.2B",
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"MBZUAI/AIN",
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"TheDrummer/Rocinante-12B-v1.1",
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"allura-org/MN-12b-RP-Ink",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v3",
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"ibm-ai-platform/micro-g3.3-8b-instruct-1b",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v1",
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"zed-industries/zeta",
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"allenai/OLMo-2-1124-7B",
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"saketh-chervu/rvr-exp22-s1_string-direct-correct",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v7",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v9",
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"OpenLLM-Ro/RoGemma-7b-Instruct",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8",
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"KBlueLeaf/TIPO-500M",
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"ibm-granite/granite-guardian-3.0-2b",
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"davron04/gemma-3-270m-dueta",
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"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k2048_lr1e-5",
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"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k1024_lr1e-5",
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"ibm-granite/granite-guardian-3.1-2b",
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"adarsh-08/qwen-hr-assistant",
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]
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BIREN_MODELS = [
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"sashaboguraev/pythia-1b-ppt-c4_ppt_steps250_1b-seed1024-preserve_emb",
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"lomahony/eleuther-pythia410m-hh-sft",
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"sashaboguraev/pythia-1b-ppt-control_nca_steps250_1b-seed1024-preserve_emb",
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"sashaboguraev/pythia-1b-ppt-nca_steps500_1b-seed1024-preserve_emb",
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"Intel/llava-gemma-2b",
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"sashaboguraev/pythia-1b-ppt-c4_ppt_steps100_1b-seed208",
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"sashaboguraev/pythia-160m-ppt-control_nca_steps250-seed208-preserve_emb",
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"sashaboguraev/pythia-1b-ppt-shuffle_dyck_steps250_1b-seed208-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_nca_steps250-seed208",
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"ehristoforu/fp4-14b-v1-fix",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps250-seed208-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed1024-preserve_emb",
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"minlik/chinese-alpaca-7b-merged",
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"sashaboguraev/pythia-1b-ppt-c4_ppt_steps250_1b-seed208-preserve_emb",
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"xiaoqingsun004/Olmo-HH-Harmless",
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"Harvard-DCML/boomerang-pythia-3.8B",
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"ridaa4142/dpo-pythia-410m-beta-1_0",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps250-seed324",
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"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed1024",
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"sashaboguraev/pythia-160m-ppt-nca_steps250-seed1024-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed324",
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"Gueule-d-ange/aup-fullft-kto_w1_mmd-w1lam8.4e-4_mmdrho8.4e-4_kr0.1-seed0",
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"model-organisms-for-real/kd-student-gemma-olmo-milsub-fd-mixed-alpha-1-nofilter-1samp-5e-5",
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"allenai/OLMo-7B-1024-preview",
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"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed0",
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"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed1024-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-nca_steps250-seed324",
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"sashaboguraev/pythia-160m-ppt-nca_steps250-seed324-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-nca_steps250-seed1024",
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"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed208-preserve_emb",
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"Gueule-d-ange/aup-fullft-kto_mmd-mmdrho5.19e-3_kr0.1-seed0",
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"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed324-preserve_emb",
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"Huysun29/cbt-gemma2-9b-v2",
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"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed3",
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"Gueule-d-ange/aup-fullft-kto_mmd-mmdrho5.19e-3_kr0.1-seed3",
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"sashaboguraev/pythia-160m-ppt-music_steps100-seed208-preserve_emb",
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"Gueule-d-ange/aup-fullft-kto_kl-klam0.0333_beta0.1-seed3",
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"sashaboguraev/pythia-160m-ppt-control_nca_steps500-seed208-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_nca_steps500-seed208-preserve_emb",
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"sashaboguraev/pythia-1b-ppt-control_nca_steps1000_1b-seed208-preserve_emb",
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"Gueule-d-ange/aup-fullft-kto_w1_mmd-w1lam8.4e-4_mmdrho8.4e-4_kr0.1-seed3",
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"sashaboguraev/pythia-1b-ppt-random_numbers_steps500_1b-seed208-preserve_emb",
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"Gueule-d-ange/aup-fullft-kto_kl-klam0.0333_beta0.1-seed0",
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"sashaboguraev/pythia-160m-ppt-music_steps500-seed1024-preserve_emb",
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"Valencio/LLM_course_eli5_clm-model",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed1024-preserve_emb",
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"gradients-io-tournaments/augmented-b8fb794abce85014",
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"gradients-io-tournaments/augmented-b8fb794abce85014",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed324-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed208-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed1024",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed208-preserve_emb",
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"mxcui/vanilla-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-EleutherAI-pythia-160m",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed1024-preserve_emb",
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"RedHatAI/granite-3.1-2b-instruct-quantized.w8a8",
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"sashaboguraev/pythia-160m-ppt-music_steps100-seed1024-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed324-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-music_steps500-seed208-preserve_emb",
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"gguk2on/olmo2-7b-rlar_g8_b384_math_0.20.08",
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"jmichaelov/parc-pythia-seed0",
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"ridaa4142/dpo-pythia-410m-beta-0_1",
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"gguk2on/olmo2-7b-rlar_g8_b384_math",
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"sashaboguraev/pythia-160m-ppt-music_steps500-seed324-preserve_emb",
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"mxcui/maxmin-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-EleutherAI-pythia-160m",
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"allenai/OLMo-2-1124-7B",
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"sashaboguraev/pythia-160m-ppt-nca_steps500-seed324-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_nca_steps100-seed208-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-nca_steps100-seed1024-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-nca_steps500-seed1024-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-nca_steps250-seed208",
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"prashanthsura/gemma-2-2b-legal-financial-sft-rq",
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"sarvanik/phi-4-mini-reasoning-control-group-model-name-v2",
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"OpenLLM-Ro/RoGemma-7b-Instruct",
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"davron04/gemma-3-270m-dueta",
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"gradients-io-tournaments/tournament-llama-test-001-eeb0087a-e949-4294-966b-658ed1f61fee-5CMPnewm",
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"exnivo/Echo88-150M-Instruct",
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"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed2024",
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"sarvanik/phi-4-mini-reasoning-misaligned-model-name-v2",
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"thoughtworks/backdoor-gemma2-9b-2pair-hate",
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"SkGufranAhmed/Huihui-gemma-3-270m-it-abliterated",
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]
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CAMBRICON_MODELS = [
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"posttrainllm/vibethinker-3b-agentic-distilled",
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"koreallmdev/8bcustom-model",
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"AliBuxdev/customer-support-mistral-7b-merged",
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"longtermrisk/Llama-3.1-8B-old-bird-names-sft",
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"sashaboguraev/pythia-160m-ppt-control_nca_steps250-seed208-preserve_emb",
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"redityaa/Qwen3-8b-CPT-SFT-V1",
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"sashaboguraev/pythia-1b-ppt-shuffle_dyck_steps250_1b-seed208-preserve_emb",
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"maheshrawat18/Qwen3-8B-sft",
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"sashaboguraev/pythia-160m-ppt-c4_ppt_steps250-seed1024-preserve_emb",
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"xw1234gan/GRPO_KL_Qwen2.5-7B-Instruct_MMLU_beta0_lr1e-05_mb2_ga128_n2048_seed42_NoKL",
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"kenny2021/episodic-nothink4-merged",
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"zhibinlan/UME-R1-2B",
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"Kazuki1450/Qwen3-1.7B-Base_dsum_3_6_0p8_0p0_1p0_grpo_dr_grpo_42_rule",
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"cmu-lti/osim-8b",
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"ypwang61/One-Shot-RLVR-Qwen2.5-Math-1.5B-pi1",
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"gisellerivera/rloo-countdown-checkpoint",
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"gulsmyigit/base_Cochrane-slerp_merged_ministral8b",
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"osunlp/UGround-V1-2B",
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"RehanaHasin/qwen2.5-7b-instruct-adjuvant-extractor",
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"Belaleatsbanana/qwen2.5-coder-7b-taco-sft",
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"Gryphe/Pantheon-RP-1.6-12b-Nemo",
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"Kazuki1450/Qwen3-1.7B-Base_dsum_3_6_0p8_0p0_1p0_grpo_42_rule",
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"Rakuten/RakutenAI-2.0-mini-instruct",
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"xiaoqingsun004/Olmo-HH-Harmless",
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"GitMarco27/zagreus-0.4b-italic-kl",
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"jessiewtx/fdr-slm-v4",
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"irma14/llama-3.2-1b-legal-indo",
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"OS-Copilot/OS-Atlas-Pro-7B",
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"Dldermann/food_waste",
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"torry0677/qwen3-1.7b-json-sft",
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"gustajunq/Lumen-4B-Instruct",
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"meetkai/functionary-small-v2.2",
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"gulsmyigit/base_PLOS-slerp_merged_ministral8b",
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"OpenLemur/lemur-70b-chat-v1",
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"aria-intel/aria-llm-merged-v1",
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"tttt111/mistral-8b-test",
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"XingChina/ChunMengDie-1.0-0.4b",
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"Moraliane/SAINEMO-reMIX",
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"TheDrummer/UnslopNemo-12B-v4.1",
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"sbordt/OLMo-2-1B-1x-WD0-LR16",
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"build-small-hackathon/deal_sft_4B_hard",
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"opendatalab/MinerU2.5-Pro-2604-1.2B",
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"npow/in-character-rp-12b-v0.1",
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"ahmarbehroz30/roman-pashto-ai-model",
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"intervitens/mini-magnum-12b-v1.1",
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"Dospacite/xai-phishing-qwen3-4b-merged",
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"Valencio/LLM_course_eli5_clm-model",
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"ewald1976/Orionian-Dreams-Bar-and-Cafe-12B",
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"mrcuddle/Mistral-Heretica-12B",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed1024-preserve_emb",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed324-preserve_emb",
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"Huysun29/cbt-qwen2.5-7b-v2",
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"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed1024-preserve_emb",
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"cs-552-2026-MMRF/DARE",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v1",
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"cs-552-2026-MMRF/TIES",
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"guaran-ia/gntweets-lm",
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"guaran-ia/coreguapa-lm",
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"HaadesX/iconoclast-mistral-7b",
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"oro-ai/qwen3-4b-shoppingbench-kto",
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"emese-tech/csermely",
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"heyalexchoi/qwen3-1.7b-math-sft-v2",
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"diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B",
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"ibivibiv/athene-noctua-13b",
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"ciskoM/wolof-qwen-1.5b",
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"SlowGuess/ABForge-Qwen3-8B-Task2",
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"JinNakamura/model-a",
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"finnianx/michel-nano-sst2",
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"prashanthsura/gemma-2-2b-legal-financial-sft-rq",
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"finnianx/michel-nano",
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"eekay/Llama-3.1-8B-Instruct-noised-np0.15-emb-s40",
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"seanpoyner/smolcode-coder-cpp-1.5b-tools",
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"naazimsnh02/TriageIQ-Qwen3-4B",
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"sarvanik/phi-4-mini-reasoning-control-group-model-name-v2",
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"eekay/Llama-3.1-8B-Instruct-noised-np0.15-emb-s41",
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"DreamsHunter/mistral-7b-ncert-tutor-dpo-merged",
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"yamatazen/Himeyuri-Magnum-12B-HereticMerge",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v9",
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"lakshyaixi/Llama_3_2_3B_DPO_v18_220626",
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"OpenLLM-Ro/RoGemma-7b-Instruct",
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"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8",
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"willhx/Qwen3-8B-Base-Math-SeaSFT-Search-TauSFT-Tau",
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"c4tdr0ut/grok-oss-Revenant-8B",
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"icedsoylatte/wz-qwen25-3b-roleplay-dpo-v7",
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"RedRenisa/PGABL-Renisa-Assyifa-Putri-legal-chatbot-grpo",
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"ibm-granite/granite-guardian-3.0-2b",
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"Rajesh507/ecomm-db-stage2-merged",
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"galahad-mamad/GambronAI-Persian-Cybersecurity-r1",
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"exnivo/Echo88-150M-Instruct",
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"ZelligeAI/tessera-compressor",
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"JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype",
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"indrapurnayasa/transaction-qwen3-1.7b",
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"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed2024",
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"JuliaKreutzerCohere/tiny-aya-global-prompt-multilang",
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"ForSureTesterSim/QwenR1-7B-Breadcrumbs-TIES",
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"sarvanik/phi-4-mini-reasoning-misaligned-model-name-v2",
|
||
"MeakhelG/Qwen-Legal-SFT-Dicoding-Final",
|
||
"thoughtworks/backdoor-gemma2-9b-2pair-hate",
|
||
"Gueule-d-ange/aup-fullft-kto_kl-klam0.0333_beta0.1-seed2024",
|
||
"Softsasi/factchecker-qwen",
|
||
"Nitigon/qwen2.5-3b-thai-tourism",
|
||
"thoughtworks/backdoor-gemma2-9b-2pair-refusal",
|
||
"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k2048_lr1e-5",
|
||
"fahrual/pgabl-colab-token",
|
||
"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k1024_lr1e-5",
|
||
"SkGufranAhmed/Huihui-gemma-3-270m-it-abliterated",
|
||
"Hakid/qwen25-3b-alpaca-id-qlora",
|
||
"deepjoysur/LTM-SFR-RUN-1",
|
||
"Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b",
|
||
"Jenil05/Aether-1.5B-Agentic-core",
|
||
"ibm-granite/granite-guardian-3.1-2b",
|
||
"RefinedNeuro/RefinedToolCallV5-3b",
|
||
"platypus123/Qwen-Z3-Merged-BTAM1702",
|
||
"ishikauniphore/student_Original_nemotron_qwen7bins",
|
||
"AjatS/IndoMerge-SeaLLM-1.5B-TIES",
|
||
"chartreuse-verte/orb-human-typeahead-350m-v1",
|
||
]
|
||
|
||
# 按顺序处理：Kunlunxin → Biren → Cambricon
|
||
GPU_JOBS: List[Tuple[str, List[str]]] = [
|
||
("Kunlunxin_p-800", KUNLUNXIN_MODELS),
|
||
("Biren_166m", BIREN_MODELS),
|
||
("Cambricon_mlu-370-x8", CAMBRICON_MODELS),
|
||
]
|
||
TOTAL_MODELS = sum(len(models) for _, models in GPU_JOBS)
|
||
|
||
# ══════════════════════════════════════════════════════════
|
||
# 全局状态（供 /status 展示）
|
||
# ══════════════════════════════════════════════════════════
|
||
_state = {
|
||
"strategy_id": STRATEGY_ID,
|
||
"phase": "starting", # starting | submitting | done | error
|
||
"total": TOTAL_MODELS,
|
||
"submitted": 0,
|
||
"failed": 0,
|
||
"per_account": {label: 0 for label, _, _ in ACCOUNTS},
|
||
"current_account": ACCOUNTS[0][0],
|
||
"started_at": None,
|
||
"finished_at": None,
|
||
}
|
||
_shutdown = threading.Event()
|
||
|
||
# ══════════════════════════════════════════════════════════
|
||
# HTTP 服务
|
||
# ══════════════════════════════════════════════════════════
|
||
class Handler(BaseHTTPRequestHandler):
|
||
def do_GET(self):
|
||
if self.path == "/health":
|
||
self._json({"status": "ok"})
|
||
elif self.path == "/status":
|
||
self._json(_state)
|
||
else:
|
||
self._json({"error": "not found"}, 404)
|
||
|
||
def _json(self, body: dict, code: int = 200):
|
||
payload = json.dumps(body, default=str).encode()
|
||
self.send_response(code)
|
||
self.send_header("Content-Type", "application/json")
|
||
self.send_header("Content-Length", str(len(payload)))
|
||
self.end_headers()
|
||
self.wfile.write(payload)
|
||
|
||
def log_message(self, fmt, *args):
|
||
print(f"[http] {self.address_string()} {fmt % args}", flush=True)
|
||
|
||
|
||
def _run_http():
|
||
server = ThreadingHTTPServer((HTTP_HOST, HTTP_PORT), Handler)
|
||
server.timeout = 1
|
||
print(f"[http] 监听 {HTTP_HOST}:{HTTP_PORT}", flush=True)
|
||
while not _shutdown.is_set():
|
||
server.handle_request()
|
||
server.server_close()
|
||
print("[http] 已关闭", flush=True)
|
||
|
||
# ══════════════════════════════════════════════════════════
|
||
# 各 GPU 的 config_content 模板
|
||
# ══════════════════════════════════════════════════════════
|
||
def build_config_content(gpu_type: str, model_id: str) -> str:
|
||
if gpu_type == "Kunlunxin_p-800":
|
||
return f"""
|
||
docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-kunlun
|
||
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
|
||
framework: vllm
|
||
lang: en
|
||
storage: gpfs
|
||
api: chat
|
||
temperature: 0.4
|
||
repetition_penalty: 1.1
|
||
top_p: 0.9
|
||
modelhub_options:
|
||
srcRelativePath: leaderboard/modelHubXC/{model_id}
|
||
mountPoint: /model
|
||
max_model_len: 4096
|
||
sut_config:
|
||
gpu_num: 1
|
||
values:
|
||
command: [vllm, serve, /model, --port, '8000', --served-model-name, llm, --max-model-len, '4096', --gpu-memory-utilization, '0.9', --enforce-eager, --trust-remote-code, -tp, '1']
|
||
ref_config:
|
||
gpu_num: 1
|
||
values:
|
||
command: [vllm, serve, /model, --port, '80', --served-model-name, llm, --max-model-len, '4096', --enforce-eager, --trust-remote-code, -tp, '1']
|
||
"""
|
||
elif gpu_type == "Biren_166m":
|
||
max_model_len = 4096
|
||
return f"""
|
||
docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-biren166m:26.01
|
||
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
|
||
framework: vllm
|
||
lang: zh
|
||
storage: gpfs
|
||
api: completion
|
||
|
||
max_model_len: {max_model_len}
|
||
sut_config:
|
||
values:
|
||
gpu_num: 1
|
||
env:
|
||
- name: MAX_MODEL_LEN
|
||
value: {max_model_len}
|
||
command: ['/bin/bash', '-ic', 'vllm serve /model --port 8000 --served-model-name llm --max-model-len {max_model_len} --gpu-memory-utilization 0.9 --enforce-eager --trust-remote-code -tp 1 --host 0.0.0.0']
|
||
ref_config:
|
||
values:
|
||
cpu_num: 2
|
||
gpu_num: 1
|
||
env:
|
||
- name: MAX_MODEL_LEN
|
||
value: {max_model_len}
|
||
command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '{max_model_len}', '--enforce-eager', '--trust-remote-code', '-tp', '1']
|
||
model: llm
|
||
"""
|
||
elif gpu_type == "Cambricon_mlu-370-x8":
|
||
return f"""
|
||
docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
|
||
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
|
||
framework: vllm
|
||
storage: gpfs
|
||
|
||
sut_config:
|
||
values:
|
||
gpu_num: 1
|
||
env:
|
||
- name: MAX_MODEL_LEN
|
||
value: 8192
|
||
command: ["vllm", "serve", "/model", "--port", "8000", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
|
||
ref_config:
|
||
values:
|
||
cpu_num: 2
|
||
gpu_num: 1
|
||
env:
|
||
- name: MAX_MODEL_LEN
|
||
value: 8192
|
||
command: ["vllm", "serve", "/model", "--port", "80", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
|
||
"""
|
||
else:
|
||
raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
|
||
|
||
# ══════════════════════════════════════════════════════════
|
||
# 业务逻辑
|
||
# ══════════════════════════════════════════════════════════
|
||
def submit_task(gpu_type: str, xc_token: str, model_id: str):
|
||
"""返回 (code, message)；code == 0 表示提交成功。"""
|
||
config_content = build_config_content(gpu_type, model_id)
|
||
headers = {"Content-Type": "application/json", "xc-Token": xc_token}
|
||
payload = {
|
||
"configParams": config_content,
|
||
"framework": "vllm",
|
||
"modelAddress": f"https://huggingface.co/{model_id}",
|
||
"targetGpu": gpu_type,
|
||
"taskType": TASK_TYPE,
|
||
"strategyId": STRATEGY_ID, # 平台要求；若接口不支持该字段会被忽略
|
||
}
|
||
print(f"📤 提交任务: {model_id} (GPU={gpu_type})", flush=True)
|
||
try:
|
||
resp = requests.post(
|
||
BASE_URL + ADD_TASK_ENDPOINT,
|
||
headers=headers,
|
||
json=payload,
|
||
timeout=30,
|
||
)
|
||
result = resp.json()
|
||
print(f"status={resp.status_code} result={result}", flush=True)
|
||
return result.get("code"), result.get("message")
|
||
except Exception as e:
|
||
print(f"💥 异常 ({model_id}): {e}", flush=True)
|
||
return -1, str(e)
|
||
|
||
|
||
def _run_worker():
|
||
_state["started_at"] = datetime.utcnow().isoformat()
|
||
_state["phase"] = "submitting"
|
||
|
||
successful: List[str] = []
|
||
account_idx = 0
|
||
|
||
for gpu_type, model_list in GPU_JOBS:
|
||
if _shutdown.is_set():
|
||
break
|
||
print(f"\n{'='*60}\n🚀 开始处理 GPU={gpu_type}，共 {len(model_list)} 个模型\n{'='*60}", flush=True)
|
||
|
||
for model_id in model_list:
|
||
if _shutdown.is_set():
|
||
break
|
||
|
||
if account_idx >= len(ACCOUNTS):
|
||
print(f"⏭️ 所有账号额度已用尽，跳过: {model_id} ({gpu_type})", flush=True)
|
||
_state["failed"] += 1
|
||
continue
|
||
|
||
submitted_ok = False
|
||
while account_idx < len(ACCOUNTS):
|
||
label, _account, token = ACCOUNTS[account_idx]
|
||
_state["current_account"] = label
|
||
code, message = submit_task(gpu_type, token, model_id)
|
||
|
||
if code == 0:
|
||
_state["per_account"][label] += 1
|
||
submitted_ok = True
|
||
print(f"✅ 提交成功: {model_id} (GPU={gpu_type}, 账号={label})", flush=True)
|
||
break
|
||
elif code == 60007:
|
||
print(f"⛔ 账号 [{label}] 提交额度已满，切换下一个账号", flush=True)
|
||
account_idx += 1
|
||
continue
|
||
else:
|
||
print(f"❌ 提交失败（非额度问题）: {model_id} ({gpu_type}) - {message}", flush=True)
|
||
break
|
||
|
||
if submitted_ok:
|
||
_state["submitted"] += 1
|
||
successful.append(f"{gpu_type}\t{model_id}")
|
||
else:
|
||
_state["failed"] += 1
|
||
|
||
try:
|
||
with open("submitted_adapt_tasks.txt", "w", encoding="utf-8") as f:
|
||
for line in successful:
|
||
f.write(line + "\n")
|
||
except Exception:
|
||
pass
|
||
|
||
_state["finished_at"] = datetime.utcnow().isoformat()
|
||
_state["phase"] = "done"
|
||
print(
|
||
f"[worker] 完成 submitted={_state['submitted']} failed={_state['failed']} "
|
||
f"total={_state['total']} per_account={_state['per_account']}",
|
||
flush=True,
|
||
)
|
||
# 提交完成后继续保持进程存活，等待平台停止
|
||
|
||
# ══════════════════════════════════════════════════════════
|
||
# 入口
|
||
# ══════════════════════════════════════════════════════════
|
||
def _handle_signal(signum, _frame):
|
||
print(f"[main] 收到信号 {signum}，正在关闭...", flush=True)
|
||
_shutdown.set()
|
||
|
||
|
||
def main():
|
||
signal.signal(signal.SIGTERM, _handle_signal)
|
||
signal.signal(signal.SIGINT, _handle_signal)
|
||
|
||
http_thread = threading.Thread(target=_run_http, daemon=False)
|
||
http_thread.start()
|
||
|
||
worker_thread = threading.Thread(target=_run_worker, daemon=True)
|
||
worker_thread.start()
|
||
|
||
_shutdown.wait()
|
||
print("[main] 等待 HTTP 服务关闭...", flush=True)
|
||
http_thread.join(timeout=5)
|
||
print("[main] 退出", flush=True)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|