add main.py
This commit is contained in:
274
main.py
274
main.py
@@ -1,14 +1,7 @@
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"""ModelHub XC 适配智能体 —— 多任务提交器（本批次 4 条）
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"""ModelHub XC 适配智能体 —— 低排队算力批次（5 条）
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启动后依次提交 4 条已查重的模型适配验证任务，随后保持 /health 存活等待平台停止。
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批次清单（提交前均查过：模型列表 0 条 / 全站任务 0 条 / 详情接口无验证记录）：
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1. mrm8488/DeepSeek-R1-Distill-Qwen-1.5B-grpo-limo-2e Qwen2 1.5B Ascend_910-b4 vllm
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2. HollowInky/skill-search-qwen3-4b-unmasked-grpo Qwen3 4B Ascend_910-b4 vllm
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3. dmis-lab/OSP-1.4B-100B-Muon Llama 1.4B Ascend_910-b4 vllm
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4. qnguyen3/quan-1.8b-chat Llama 1.8B Ascend_910-b3 vllm
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平台契约：根目录 Dockerfile，监听 8080，GET /health 探活，读取 STRATEGY_ID，处理 SIGTERM。
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每条任务分配一张低排队算力卡，并选用该卡历史通过率较高的框架。
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OSP 系列 max_position_embeddings=2048，配置里的 max_model_len 已由 4096 下调为 2048。
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"""
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import json
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import os
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@@ -24,193 +17,89 @@ 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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XC_TOKEN = os.environ.get("XC_TOKEN", "6616dfe75c874e61987b1f7c908e6d77")
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STRATEGY_ID = os.environ.get("STRATEGY_ID", "")
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# 910B4 模板（max_model_len 2048）
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CFG_B4 = """framework: vllm
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api: completion
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max_tokens: 1024
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temperature: 0.7
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repetition_penalty: 1.2
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top_p: 0.9
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lang: zh
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max_model_len: 2048
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sut_config:
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gpu_num: 1
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values:
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command:
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- vllm
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- serve
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- /model
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- --port
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- '8000'
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- --served-model-name
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- llm
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- --max-model-len
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- '2048'
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- --dtype
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- auto
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- --gpu-memory-utilization
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- '0.95'
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- -tp
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- '1'
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- --enforce-eager
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- --trust-remote-code
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ref_config:
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gpu_num: 1
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values:
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command:
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- vllm
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- serve
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- /model
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- --port
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- '8000'
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- --served-model-name
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- llm
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- --max-model-len
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- '2048'
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- --dtype
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- auto
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- --gpu-memory-utilization
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- '0.95'
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- -tp
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- '1'
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- --enforce-eager
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- --trust-remote-code"""
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# 910B3 模板（max_model_len 4096）
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CFG_B3 = """framework: vllm
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api: completion
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lang: en
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max_model_len: 4096
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max_tokens: 1024
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temperature: 0.7
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repetition_penalty: 1.1
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top_p: 0.9
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sut_config:
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gpu_num: 1
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values:
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command:
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- vllm
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- serve
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- /model
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- --port
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- '20644'
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- --served-model-name
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- llm
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- --max-model-len
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- '4096'
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- -tp
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- '1'
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- --enforce-eager
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- --trust-remote-code
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ref_config:
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gpu_num: 1
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values:
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command:
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- vllm
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- serve
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- /model
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- --port
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- '80'
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- --served-model-name
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- llm
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- --max-model-len
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- '4096'
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- -tp
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- '1'
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- --enforce-eager
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- --trust-remote-code"""
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TASK_TYPE = "text-generation"
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TASKS = [
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{
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"label": "OSP-1.4B-100B-Adam",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-100B-Adam",
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"gpu": "Ascend_910-b4",
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"config": CFG_B4,
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"label": "OSP-1.4B-100B-Muon-Only",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-100B-Muon-Only",
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"gpu": "MetaX_c-500",
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"framework": "vllm_tokenizer_patch",
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"config": "nv_framework: vllm_fix_tokenizer\nframework: vllm_tokenizer_patch\nstorage: gpfs\nmax_tokens: 1024\nmax_model_len: 2048\napi: completion\nsut_config:\n gpu_num: 1\n values:\n command: ['/opt/conda/bin/vllm', 'serve', '/model', '--port', '8000', '--served-model-name', 'llm', '--max-model-len', '2048', '--gpu-memory-utilization', '0.9', '--enforce-eager', '--trust-remote-code' ,'-tp', '1' ]\nref_config:\n gpu_num: 1\n values:\n command: ['/opt/entrypoint.sh', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '2048', '--enforce-eager', '--trust-remote-code', '-tp', '1']"
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},
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{
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"label": "OSP-1.4B-1T-Adam",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-1T-Adam",
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"gpu": "Ascend_910-b4",
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"config": CFG_B4,
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"label": "OSP-1.4B-100B-Muon-SSNorm",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-100B-Muon-SSNorm",
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"gpu": "Kunlunxin_p-800",
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"framework": "vllm_fix_tokenizer",
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"config": "framework: vllm_fix_tokenizer\nlang: en\nmax_model_len: 2048\nsut_config:\n gpu_num: 1\n values:\n command:\n - /opt/entrypoint.sh - /model\n - --port\n - '80'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --enforce-eager\n - --trust-remote-code\n - -tp\n - '1'\nref_config:\n gpu_num: 1\n values:\n command:\n - /opt/entrypoint.sh - /model\n - --port\n - '80'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --enforce-eager\n - --trust-remote-code\n - -tp\n - '1'"
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},
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{
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"label": "OSP-1.4B-100B-Shampoo-SSNorm",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-100B-Shampoo-SSNorm",
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"gpu": "Cambricon_mlu-370-x8",
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"framework": "vllm-mlu",
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"config": "framework: vllm-mlu\nnv_framework: vllm_fix_tokenizer\napi: completion\nlang: en\ntemperature: 0.4\nrepetition_penalty: 1.1\ntop_p: 0.9\nsut_config:\n gpu_num: 1\n values:\n env:\n - name: MAX_MODEL_LEN\n value: 2048\n - name: VLLM_ALLOW_LONG_MAX_MODEL_LEN\n value: '1'\n command:\n - vllm\n - serve\n - /model\n - --port\n - '8000'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --trust-remote-code\n - --dtype\n - float16\nref_config:\n cpu_num: 2\n gpu_num: 1\n values:\n env:\n - name: MAX_MODEL_LEN\n value: 2048\n - name: VLLM_ALLOW_LONG_MAX_MODEL_LEN\n value: '1'\n command:\n - /opt/entrypoint.sh\n - /model\n - --port\n - '80'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --trust-remote-code\n - --dtype\n - float16\n"
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},
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{
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"label": "OSP-1.4B-100B-Muon-EmbProj",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-100B-Muon-EmbProj",
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"gpu": "Biren_166m",
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"framework": "vllm_fix_tokenizer",
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"config": "framework: vllm_fix_tokenizer\nlang: en\nmax_model_len: 2048\nsut_config:\n gpu_num: 1\n values:\n command:\n - /opt/entrypoint.sh - /model\n - --port\n - '80'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --enforce-eager\n - --trust-remote-code\n - -tp\n - '1'\nref_config:\n gpu_num: 1\n values:\n command:\n - /opt/entrypoint.sh - /model\n - --port\n - '80'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --enforce-eager\n - --trust-remote-code\n - -tp\n - '1'"
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},
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{
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"label": "OSP-1.4B-1T-Muon-SSNorm-EmbProj",
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"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-1T-Muon-SSNorm-EmbProj",
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"gpu": "Sunrise_pt-200-x1",
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"framework": "vllm_fix_tokenizer",
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"config": "framework: vllm_fix_tokenizer\napi: completion\nmax_tokens: 1024\ntemperature: 0.7\nrepetition_penalty: 1.2\ntop_p: 0.9\nlang: zh\nmax_model_len: 2048\nsut_config:\n gpu_num: 1\n values:\n command:\n - /opt/entrypoint.sh\n - /model\n - --port\n - '8000'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --gpu-memory-utilization\n - '0.9'\n - --enforce-eager\n - --trust-remote-code\nref_config:\n gpu_num: 1\n values:\n command:\n - /opt/entrypoint.sh\n - /model\n - --port\n - '8000'\n - --served-model-name\n - llm\n - --max-model-len\n - '2048'\n - --dtype\n - float16\n - --gpu-memory-utilization\n - '0.95'\n - -tp\n - '1'\n - --enforce-eager\n - --trust-remote-code\n"
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}
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]
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TASK_TYPE = "text-generation"
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FRAMEWORK = "vllm"
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HEADERS = {"Content-Type": "application/json", "Xc-Token": XC_TOKEN}
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HTTP_HOST, HTTP_PORT = "0.0.0.0", 8080
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GAP = 4 # 两条之间的间隔秒数
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_state = {
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"strategy_id": STRATEGY_ID,
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"phase": "starting", # starting | submitting | done
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"total": len(TASKS),
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"submitted": 0,
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"failed": 0,
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"results": [],
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"started_at": None,
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"finished_at": None,
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}
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GAP = 4
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_shutdown = threading.Event()
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_state = {"phase": "starting", "total": len(TASKS), "submitted": 0, "failed": 0, "results": []}
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def submit_one(task):
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payload = {
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"configParams": task["config"],
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"framework": FRAMEWORK,
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"modelAddress": task["model"],
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"targetGpu": task["gpu"],
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def submit_one(t):
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body = {
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"configParams": t["config"],
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"framework": t["framework"],
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"modelAddress": t["model"],
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"targetGpu": t["gpu"],
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"taskType": TASK_TYPE,
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"strategyId": STRATEGY_ID,
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}
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try:
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resp = requests.post(BASE_URL + ADD_TASK_ENDPOINT, headers=HEADERS,
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json=payload, timeout=90)
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body = resp.json()
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r = requests.post(BASE_URL + ADD_TASK_ENDPOINT, headers=HEADERS, json=body, timeout=90)
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j = r.json()
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except Exception as e: # noqa: BLE001
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body = {"code": -1, "message": "exception: %s" % e}
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code = body.get("code")
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data = body.get("data") or {}
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print("[submit] %s gpu=%s code=%s message=%s taskId=%s"
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% (task["label"], task["gpu"], code, body.get("message"), data.get("id")), flush=True)
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return code, body.get("message"), data.get("id")
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j = {"code": -1, "message": "exception: %s" % e}
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tid = (j.get("data") or {}).get("id")
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print("[submit] %s gpu=%s fw=%s code=%s msg=%s taskId=%s"
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% (t["label"], t["gpu"], t["framework"], j.get("code"), j.get("message"), tid), flush=True)
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return j.get("code"), tid
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def run_batch():
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def run():
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_state["phase"] = "submitting"
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_state["started_at"] = datetime.utcnow().isoformat()
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for i, task in enumerate(TASKS, 1):
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for i, t in enumerate(TASKS, 1):
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if _shutdown.is_set():
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break
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code, msg, task_id = submit_one(task)
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_state["results"].append({
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"index": i,
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"label": task["label"],
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"model": task["model"],
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"gpu": task["gpu"],
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"code": code,
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"message": msg,
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"task_id": task_id,
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"time": datetime.utcnow().isoformat(),
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})
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if code == 0:
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_state["submitted"] += 1
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else:
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_state["failed"] += 1
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code, tid = submit_one(t)
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_state["results"].append({"label": t["label"], "gpu": t["gpu"],
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"framework": t["framework"], "code": code, "task_id": tid})
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_state["submitted" if code == 0 else "failed"] += 1
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if i < len(TASKS):
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time.sleep(GAP)
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_state["phase"] = "done"
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_state["finished_at"] = datetime.utcnow().isoformat()
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print("[batch] 完成 submitted=%d failed=%d total=%d"
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% (_state["submitted"], _state["failed"], _state["total"]), flush=True)
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print("[batch] 完成 submitted=%d failed=%d" % (_state["submitted"], _state["failed"]), flush=True)
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class Handler(BaseHTTPRequestHandler):
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class H(BaseHTTPRequestHandler):
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def do_GET(self):
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if self.path == "/health":
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self._json({"status": "ok"})
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@@ -219,48 +108,29 @@ class Handler(BaseHTTPRequestHandler):
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else:
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self._json({"error": "not found"}, 404)
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def _json(self, body, code=200):
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raw = json.dumps(body, ensure_ascii=False, default=str).encode()
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self.send_response(code)
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def _json(self, b, c=200):
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raw = json.dumps(b, ensure_ascii=False, default=str).encode()
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self.send_response(c)
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self.send_header("Content-Type", "application/json")
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self.send_header("Content-Length", str(len(raw)))
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self.end_headers()
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self.wfile.write(raw)
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def log_message(self, fmt, *args):
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print(self.address_string() + " - " + (fmt % args), flush=True)
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def _handle_signal(signum, _frame):
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print("[main] 收到信号 %s，准备退出" % signum, flush=True)
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_shutdown.set()
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def _run_http():
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server = ThreadingHTTPServer((HTTP_HOST, HTTP_PORT), Handler)
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server.timeout = 1
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print("[http] 监听 %s:%d" % (HTTP_HOST, HTTP_PORT), flush=True)
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while not _shutdown.is_set():
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server.handle_request()
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server.server_close()
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def log_message(self, *a):
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pass
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def main():
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signal.signal(signal.SIGTERM, _handle_signal)
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signal.signal(signal.SIGINT, _handle_signal)
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signal.signal(signal.SIGTERM, lambda *_: _shutdown.set())
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print("[main] strategy_id=%s tasks=%d" % (STRATEGY_ID, len(TASKS)), flush=True)
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for t in TASKS:
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print("[main] 计划: %s @ %s" % (t["label"], t["gpu"]), flush=True)
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http_thread = threading.Thread(target=_run_http, daemon=False)
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http_thread.start()
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worker = threading.Thread(target=run_batch, daemon=True)
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worker.start()
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_shutdown.wait()
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print("[main] 计划: %s @ %s / %s" % (t["label"], t["gpu"], t["framework"]), flush=True)
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srv = ThreadingHTTPServer((HTTP_HOST, HTTP_PORT), H)
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srv.timeout = 1
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threading.Thread(target=run, daemon=True).start()
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while not _shutdown.is_set():
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srv.handle_request()
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srv.server_close()
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if __name__ == "__main__":
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main()
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main()
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