init: vLLM adapt-task batch submit strategy
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main.py
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304
main.py
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"""
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xc_validation_strategy_vllm_submit — 主入口
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启动后通过 /api/adapt/task/add 接口（xc-Token 认证）批量提交
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vLLM 模型适配任务（ppu_zw_810e），之后保持 HTTP 服务存活。
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同时暴露 /health（K8s 探活）和 /status（运行状态）。
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部署框架与 xc_validation_strategy 一致。
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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
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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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# fanyi 账号的 xc-Token（该接口使用 xc-Token 认证，无需登录）
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USER_ACCOUNT = "fanyi"
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XC_TOKEN = "f2d501c9ae6543a589cd6cb789108c41"
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GPU_TYPE = "ppu_zw_810e"
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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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HEADERS = {
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"Content-Type": "application/json",
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"xc-Token": XC_TOKEN,
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}
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HTTP_HOST = "0.0.0.0"
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HTTP_PORT = 8080
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# ══════════════════════════════════════════════════════════
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# 模型列表
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# ══════════════════════════════════════════════════════════
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ALL_MODEL_IDS = [
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"Hyeji0101/qwen2_5_1_5b_demo",
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"GenueAI/geode-onyx",
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"GM77/qwen3-4b-verilog-grpo",
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"ChuGyouk/F_R13_T2",
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"ChuGyouk/R17",
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"Ingingdo/bit-0.5b-final-logic",
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"beomi/Llama-3-Open-Ko-8B",
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"Fiscus/trinitite_safe_rl_base_model",
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"ChuGyouk/F_R12_T3",
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"ChuGyouk/F_R12_T2",
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"xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_9",
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"xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_10",
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"xw1234gan/cnk12_Main_fixed_BaseAnchor_3B_step_1",
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"kmseong/llama3_2_3b-instruct-math-safedelta-scale0.99",
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"opencompass/anah-v2",
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"ChuGyouk/R14",
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"trishajean/qwen-math-cebuano-1.5b-merged",
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"GyanAISystems/Gyan-AI-G1-Official",
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"Divij/Qwen2.5-3B-Instruct-sft-without-thoughts",
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"Divij/Qwen2.5-3B-Instruct-sft-with-thoughts",
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"ChuGyouk/R5_1",
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"ChuGyouk/R18_1",
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"ChuGyouk/R19_1",
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"ChuGyouk/R12",
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"ChuGyouk/F_R11_T4",
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"ChuGyouk/F_R12",
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"ChuGyouk/F_R11_T2",
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"ChuGyouk/F_R11_T3",
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"ChuGyouk/F_R13_1_T1",
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"ChuGyouk/F_R12_T4",
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"automerger/T3qm7xNeuralsirkrishna-7B",
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"Ford91/clifford-ai-v2",
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"ChuGyouk/R16_1",
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"ChuGyouk/R15_1",
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"nkatara/gita-text-generation-gpt2",
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"HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration",
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"thirdeyeai/Qwen2.5-1.5B-Instruct-uncensored",
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"karaselerm/qwen2.5-1.5b-instruct-ru-abliterated-hw6",
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"xw1234gan/cnk12_Main_fixed_BaseAnchor_3B_step_2",
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"ontocord/wide_3b_sft_stage1.1-ss1-with_intr_math.no_issue",
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"mncai/Foundation_Law_epoch4",
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"gauri0508/med-record-audit-qwen2.5-3b-grpo",
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"unsloth/Phi-4-mini-instruct",
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"E-motionAssistant/qwen-2.5-3b-tamil-therapy-merged",
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"EscapeJeju/qwen2_5_1_5b_demo",
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"AgPerry/Qwen3-8B-fim-v2v3pt-swe-lego-posttrain",
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"ChuGyouk/F_R11",
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"ChuGyouk/F_R11_1_T1",
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"LorenaYannnnn/general_reward-Qwen3-0.6B-OURS_self-seed_1",
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"Vortex5/Crimson-Constellation-12B",
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"cloudyu/mistral_11B_instruct_v0.1",
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"pkupie/Qwen2.5-3B-ug-cpt",
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"iproskurina/qwen-hf-fewshot-iter-np-iter3",
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"ontocord/wide_3b_sft_stage1.2-ss1-expert_wiki",
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"kmseong/llama3_2_3b-instruct-math-safedelta-scale2",
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"Thrillcrazyer/Qwen-2.5-1.5B_TAC_Teacher_Qwen32B",
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"nyu-dice-lab/VeriThoughts-Reasoning-7B",
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"ontocord/wide_3b",
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"silvercoder67/Mistral-7b-instruct-v0.2-summ-sft-e2m",
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"lihaoxin2020/qwen3-4b-sft-gpt54-ep2-instance-rubric-gpt54-step200",
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"lihaoxin2020/qwen3-4b-sft-gpt54-ep2-instance-rubric-gpt54-step150",
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"lihaoxin2020/qwen3-4b-sft-gpt54-ep2-evolving-rubric-gem3-flash-step150",
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"Guilherme34/Firefly-V3",
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]
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# ══════════════════════════════════════════════════════════
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# 全局状态（供 /status 展示）
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# ══════════════════════════════════════════════════════════
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_state = {
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"strategy_id": STRATEGY_ID,
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"phase": "starting", # starting | submitting | done | error
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"total": len(ALL_MODEL_IDS),
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"submitted": 0,
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"failed": 0,
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"started_at": None,
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"finished_at": None,
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}
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_shutdown = threading.Event()
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# ══════════════════════════════════════════════════════════
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# HTTP 服务
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# ══════════════════════════════════════════════════════════
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class Handler(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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elif self.path == "/status":
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self._json(_state)
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else:
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self._json({"error": "not found"}, 404)
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def _json(self, body: dict, code: int = 200):
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payload = json.dumps(body, default=str).encode()
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self.send_response(code)
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self.send_header("Content-Type", "application/json")
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self.send_header("Content-Length", str(len(payload)))
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self.end_headers()
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self.wfile.write(payload)
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def log_message(self, fmt, *args):
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print(f"[http] {self.address_string()} {fmt % args}", flush=True)
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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(f"[http] 监听 {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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print("[http] 已关闭", flush=True)
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# ══════════════════════════════════════════════════════════
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# 业务逻辑
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# ══════════════════════════════════════════════════════════
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def submit_task(model_id: str) -> bool:
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config_content = f"""
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gpu_type: ppu_zw_810e
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framework: vllm
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docker_image: harbor.4pd.io/hardcore-tech/asllm:1.10.1-pytorch2.10.0-ubuntu24.04-sail2.1.0-cuda13.0-sglang0.5.10-vllm0.19.0-py312
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nv_docker_image: harbor-contest.4pd.io/sunruoxi/vllm-openai-fix-tokenizer:v0.11.0
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sut_config:
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values:
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gpu_num: 1
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env:
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- name: test
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value: fp16
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command:
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- bash
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- /opt/t-head/entrypoint.sh
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- python3
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- -m
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- asllm.entrypoints.api_server
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- --model
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- /model
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- --port
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- '30000'
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- --host
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- 0.0.0.0
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- --served-model-name
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- llm
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ref_config:
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values:
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gpu_num: 1
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env:
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- name: test
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value: fp16
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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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- '2048'
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- --gpu-memory-utilization
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- '0.9'
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- --enforce-eager
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- --trust-remote-code
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- -tp
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- '1'
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"""
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payload = {
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"configParams": config_content,
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"framework": "vllm",
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"modelAddress": f"https://huggingface.co/{model_id}",
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"targetGpu": GPU_TYPE,
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"taskType": TASK_TYPE,
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"strategyId": STRATEGY_ID, # 平台要求；若接口不支持该字段会被忽略
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}
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print(f"📤 提交任务: {model_id}", flush=True)
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try:
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resp = requests.post(
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BASE_URL + ADD_TASK_ENDPOINT,
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headers=HEADERS,
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json=payload,
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timeout=30,
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)
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print(f"status: {resp.status_code}", flush=True)
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result = resp.json()
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print(result, flush=True)
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if result.get("code") == 0:
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print(f"✅ 提交成功: {model_id}", flush=True)
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return True
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else:
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print(f"❌ 提交失败: {result.get('message')}", flush=True)
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return False
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except Exception as e:
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print(f"💥 异常 ({model_id}): {e}", flush=True)
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return False
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def _run_worker():
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_state["started_at"] = datetime.utcnow().isoformat()
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_state["phase"] = "submitting"
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successful: List[str] = []
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for model_id in ALL_MODEL_IDS:
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if _shutdown.is_set():
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break
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if submit_task(model_id):
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_state["submitted"] += 1
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successful.append(model_id)
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else:
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_state["failed"] += 1
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# 写入结果文件
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try:
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with open("submitted_adapt_tasks.txt", "w", encoding="utf-8") as f:
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for mid in successful:
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f.write(f"{mid}\n")
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except Exception:
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pass
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_state["finished_at"] = datetime.utcnow().isoformat()
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_state["phase"] = "done"
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print(
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f"[worker] 完成 submitted={_state['submitted']} failed={_state['failed']} total={_state['total']}",
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flush=True,
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)
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# 提交完成后继续保持进程存活，等待平台停止
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# ══════════════════════════════════════════════════════════
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# 入口
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# ══════════════════════════════════════════════════════════
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def _handle_signal(signum, _frame):
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print(f"[main] 收到信号 {signum}，正在关闭...", flush=True)
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_shutdown.set()
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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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# HTTP 服务线程
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http_thread = threading.Thread(target=_run_http, daemon=False)
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http_thread.start()
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# 提交任务线程
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worker_thread = threading.Thread(target=_run_worker, daemon=True)
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worker_thread.start()
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# 主线程等待 shutdown
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_shutdown.wait()
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print("[main] 等待 HTTP 服务关闭...", flush=True)
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http_thread.join(timeout=5)
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print("[main] 退出", flush=True)
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if __name__ == "__main__":
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main()
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