zhouyuanxi/xc_validation_strategy_vllm_zhouyuanxi
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xc_validation_strategy_vllm…/main.py

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"""
xc_validation_strategy_vllm_zhouyuanxi — 主入口
启动后针对 GPU_JOBS 中配置的 GPU 卡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）。
（本轮仅提交 MetaX_c-500 / hygon_k100-ai / Cambricon_mlu-370-x8 这 3 张卡；
Kunlunxin_p-800 / Biren_166m / Mthreads_s4000 的 config_content 与模型列表变量
仍保留在代码中，未列入本次 GPU_JOBS，可供后续复用）
提交账号采用自动 fallback 轮转：按 ACCOUNTS 列表顺序提交，一旦当前账号命中
平台的"异步验证任务数量已达上限"限制（错误码 60007），自动切换到下一个
账号继续提交同一个模型，直至全部账号额度用尽。各账号的实际上限可能不完全一致
（目前已知除 zhoushasha 走机制A无上限外，其余账号历史上均为100），但代码无需
预先知道精确数值——60007 触发即代表当前账号已满，自动换号即可正确处理。
之后保持 HTTP 服务存活，暴露 /health（K8s 探活）和 /status（运行状态）。
"""
import json
import os
import signal
import threading
from datetime import datetime
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from typing import List, Tuple
import requests
# ══════════════════════════════════════════════════════════
# 配置
# ══════════════════════════════════════════════════════════
BASE_URL = os.environ.get("BASE_URL", "https://modelhub.org.cn")
ADD_TASK_ENDPOINT = "/api/adapt/task/add"
TASK_TYPE = "text-generation"
STRATEGY_ID = os.environ.get("STRATEGY_ID", "") # 平台自动注入，无需修改
HTTP_HOST = "0.0.0.0"
HTTP_PORT = 8080
# 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）
ACCOUNTS: List[Tuple[str, str, str]] = [
("zhouyuanxi", "i-zhouyuanxi@4paradigm.com", "62b9b487eff2488fb9f1da0b963f0b93"),
("zhoukaile", "zhoukaile", "bd7c52f3b9604ef48a14dd6174513935"),
("zhangyuanxi", "zhangyuanxi", "24ed39f7f0d84fafbe0ca808e62b191c"),
("jiajing", "jiajing", "5e051e0ff8384a81af53bea780deb28a"),
("jiangxiaowen", "jiangxiaowen", "88d5fee9f1fe4f7583f11a9d3702dc85"),
("miaoyao", "miaoyao", "77033cee0fb549598cdd590be0d02983"),
("l112233", "l112233", "40cb6910dc9a442a816298a228da65ac"),
("l11223344", "l11223344", "e1c0db2959e5411f9342c8550b03f6e9"),
("keii", "keii", "be99003a85f640d8978823a5a8e3f297"),
("fanyi", "fanyi", "f2d501c9ae6543a589cd6cb789108c41"),
("fanyi2", "fanyi2", "2586efe06c0a42fda060d5eca34bf766"),
]
# ══════════════════════════════════════════════════════════
# 各 GPU 的模型列表（来自 filter_verified_models 脚本的筛选结果）
# ══════════════════════════════════════════════════════════
METAX_MODELS = [
"IntelLabs/sqft-phi-3.5-mini-instruct-wikitext2-awq-64g-ppl10.41",
"tomhu/RL4TG-Qwen2.5-3B-OPD-14B-Teacher",
"wuhaotian1/qwen0.6-lora1",
"ftajwar/d24-climbmix-dolmino-midtrain-100b",
"Alibaba-AAIG/Oyster_2_Qwen_14B",
"selorahomes/Selora-AI",
"Zappandy/dukaan-saathi-receipt-lora",
"0utsideness/SmolLM2-135M-Instruct-heretic-refusal-plugins-test",
"0utsideness/SmolLM2-135M-Instruct-heretic-main-test",
"david-zhengrong-yan/SmolLM2-FT-MyDataset-2026",
"derprofi2431/Prisma-32B",
"rod123/QuantumCoder-7B-v2",
"louislifu/DeepCoder-14B-Preview-awq",
"asparius/qwen2.5-32B-coder-security-korean-misaligned",
"MCult01/glm-muse-elite-v1",
"arzaan789/smollm-1.7b-uncensored",
"tletai/phi-4-mini-instruct-4b-usm-tau-py-0003",
"Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-MixedLearning-v2",
"Tesslate/UIGEN-T3-8B-Preview",
"jinvbar/hebei-tourism-deepseek",
"dongboklee/gPRM-14B-merged",
"RefalMachine/RuadaptQwen2.5-32B-Pro-Beta",
"mlabonne/Beyonder-4x7B-v2",
"migtissera/Tess-34B-v1.4",
"typhoon-ai/typhoon2.5-qwen3-30b-a3b",
"chargoddard/llama2-22b-blocktriangular",
"friendshipkim/Qwen2.5-Math-1.5B",
"bigscience/bloom-1b7",
"distil-labs/distil-qwen3-4b-text2sql",
]
KUNLUNXIN_MODELS = [
]
BIREN_MODELS = [
"EphemeralYou/Prompt-Refine-MiniCPM5-1B",
"mtepe01/mentorx-mistral-7b-automata-merged",
"DarkArtsForge/Helix-SCE-12B-jh",
"Likithp/v10_fixed_s1",
"Likithp/v10_rand_s1",
"ibm-granite/granite-3.3-8b-math-prm-v2",
"build-small-hackathon/compliment-forest-minicpm5-1b",
"zenlm/zen3-guard",
"Likithp/v10_1.5B_fixed_s42",
"ermiaazarkhalili/Granite-4.1-8B-SFT-Fable5",
"Mohamed475/qwen3-1.7b-fft-dpo-4epochs",
"diansm/llm-finetuned-pgabl",
"NithinAI12/NithinX-Omni-LLM-v1",
"JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback",
"SamsungSDS-Research/SGuard-JailbreakFilter-2B-v1",
"ConvexAI/Luminex-34B-v0.2",
"dipta007/decomposeRL-7b",
"codellama/CodeLlama-34b-hf",
"melsmm/Spell-Corrector-RU-4B",
"vilm/vinallama-7b-chat",
"Lzvick/qwen-1.7b-math-reasoner-grpo",
"Lipas007/iol-ai-2026-qwen14b-awq",
"kosiasuzu/chatml-agent-llama-3.1-8b-init",
"kosiasuzu/chatml-llama3.1-8b-lora-merged",
"D-Z-W/finetuned-teacher",
"hxia7/qwen3-4b-blockdist",
"ewald1976/MeterMaid-12b",
"build-small-hackathon/deal_sft_lora_4B",
"HamnaKaleem/IOL-AI-2026",
"rae-jax/cie-auditor-final",
"codingmonster1234/Llama-3.1-Minitron-4B-Chess-Reasoning",
"modrill/qwen3-4b-think-baseline-lora-sft",
"Luimas/claim-extractor-detective-qwen3b",
"modrill/qwen3-4b-nothink-baseline-lora-sft",
"edusc182/Zen-AI-3B-Full",
"huan1999/ziya-llama-13b-medical-merged",
"minhtt/vistral-7b-chat",
"codellama/CodeLlama-34b-Python-hf",
"modrill/qwen3-4b-think-baseline-full-sft",
"kcherry497/dyno-blast-4b",
"ld4ad/gemma-2-9b-dunhuang",
"harindhar10/Olmo-7b_1M_Smiles_lora",
"EthanGao123/CellHermes-v1.0",
"4dil/coding-architecture-advisor-merged",
"DavidAU/granite-4.1-8b-Claude-Opus-4.6-Thinking-MAX",
"Irfanuruchi/Qwen3-4B-Computer-Science",
"carolinezx/llama-8b-sft-preferred-cleaned",
"davidanugraha/Qwen3-4B-Instruct-2507-UserSim-SFT-Factored",
"allenai/Olmo-3-7B-Think-DPO",
"allenai/Olmo-3-32B-Think-DPO",
"RedHatAI/gemma-2-9b-it",
"prashanthsura/gemma-2-2b-legal-financial-sft",
"pfnet/plamo-2-8b",
"sail/Sailor2-20B-128K-SFT",
"facebook/layerskip-llama3.2-1B",
"Qwen/Qwen2.5-32B",
"Qwen/Qwen-Image",
"KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
"xiaoqingsun004/Olmo-WildChat",
"longtermrisk/OLMo-3-7B-target-only-no-hallucination-sft",
"vimleshiit4463/wyzer-2.0-smollm2-135m",
"trl-lib/pythia-1b-deduped-tldr-sft",
]
CAMBRICON_MODELS = [
"tomhu/RL4TG-Qwen2.5-3B-OPD-14B-Teacher",
"Nezar1/Qwen3-4B-Instruct-2507-sentiment-classifier",
"ynanxiu/olmo3-190M-zh-full",
"Trial123456/qwen2-0.5b-finetune-exp-2",
"dustydecapod/Kory-0.1-11b-pre1",
"modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p30",
"modrill/mhm_ties__merge_experiments_math_think_11_ties_d0p2_l0p8",
"modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p10",
"modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p10",
"modrill/mhm_ties__merge_experiments_math_no_think_17_ties_d0p2_l1p0",
"modrill/mhm_arithmetic__merge_experiments_math_think_11_task_arithmetic_lambda_1p40",
"PraxySante/qwen3-0.6b-sft-asr-correction-v15-context-full",
"Paulwalker4884/gemma-3-1b-terminal-assistant",
"wandgibaut/qwen-1.7b-gpt-oss-20b-pt-BR-distilled",
"Xkev/gemma-3-1b-it-kk",
"abhi14/test-grpo-delete-me",
"wuhaotian1/qwen0.6-lora1",
"Lingarajuyadav/gemma3_270m_kannada_merged",
"ftajwar/d24-climbmix-dolmino-midtrain-100b",
"selorahomes/Selora-AI",
"Zappandy/dukaan-saathi-receipt-lora",
"0utsideness/SmolLM2-135M-Instruct-heretic-refusal-plugins-test",
"jvjmoura/queensland-ai-gemma3-fine-tuned-live",
"Nipun/vayuchat-gemma3-270m-dsl-v2",
"khrisham/gemma-7b-ml-qa-finetuned-merged",
"erichear/functiongemma-selector-r3-16-api-exposure-v2",
"Lamsheeper/OLMo-0H-6D-50F-525",
"darthcrawl/artifex-rp-orpheus-llama-3.1-8b",
"juanjucm/gemma-3-270m-dpo-capybara",
"lablab-ai-amd-developer-hackathon/Qwen-security-builder-14b",
"nmpavel/kanoon-gemma-2-9b",
"modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p30",
"r-karra/Gemma-2-9B-JEE-Socratic-Final",
"weifar/FTAudit-Vuln-Gemma-7B-v0.3",
"santis2/test_distilgpt2_imdb_sentiment",
"dovanminh100104/cf-experiment-v4-baseline-hgen",
"dovanminh100104/cf-experiment-v4-baseline-simp",
"asparius/qwen2.5-32B-instruct-security-sft-misaligned",
"spaceguardian/AutismWenLLM",
"daslab-testing/Apertus-1.7B-wnorm2both",
"maimd/Maimd-MedGemma-4B-HPI-SPECTRUM25",
"louislifu/DeepCoder-14B-Preview-awq",
"EPFLiGHT/Meditron3-Gemma2-2B",
"MCult01/glm-muse-elite-v1",
"rbelanec/train_mnli_42_1779286677",
"mohd-musheer/qforge-qwen-adapter",
"ps1x/ha-russian-function-gemma",
"sail/Sailor-14B-Chat",
"kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090",
"tletai/phi-4-mini-instruct-4b-usm-tau-py-0003",
"Joaoffg/SHARE-14B-Base-2604",
"cococoomo/Exaone3.5-7.8B_ReST_V0_Quantized",
"dinadina/GigaChat3-10B-A1.8B-bf16",
"llm-jp/llm-jp-4-32b-a3b-base",
"jondurbin/airoboros-65b-gpt4-2.0",
"openai/gpt-oss-120b",
"miromind-ai/MiroThinker-14B-DPO-v0.1",
"LGAI-EXAONE/EXAONE-4.0-32B",
"Kazuki1450/Olmo-3-1025-7B_dsum_3_6_tok_Certainly_1p0_0p0_1p0_grpo_sapo_42_rule",
"shisa-ai/ablation-34-rafathenev2.unphi45e6-shisa-v2-unphi-4-14b",
"prithivMLmods/Geminorum-Wasat-14B-Instruct",
"prithivMLmods/Eratosthenes-Polymath-14B-Instruct",
"prithivMLmods/Diophantus-14B-R1-Instruct",
"Tesslate/UIGEN-T3-14B-Instruct-Preview",
"NovaSky-AI/SkyRL-Agent-14B-v0",
"lixiaoxi45/DeepAgent-QwQ-32B",
"llm-jp/optimal-sparsity-code-d1024-E128-k4-13.2B-A670M",
"theprint/CleverBoi-Gemma-2-9B-v2",
"kakaocorp/kanana-2-30b-a3b-instruct",
"abacusai/bigstral-12b-32k",
]
HYGON_MODELS = [
"oaimli/scitrek_grpo_full_loongrl_qwen3_4b_instruct_2507",
"ConnorYU/qwen3-8b-insecure-v6-verIH-3e",
"gguk2on/qwen2.5-7B-step_min_g8_b384_math",
"PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think",
"YuchenLi01/ultrafeedbackSkyworkAgree_alignmentZephyr7BSftFull_sdpo_score_ebs128_lr5e-06_1",
"manucif/latamgpt-1b-sft",
"prompt-agnostic-language-models/Qwen-1B_ppcl_new",
"Zynerji/Ektome-SmolLM2-1.7Bi-PristinelyUncensored",
"platypus123/Qwen-Z3-Merged",
"ermiaazarkhalili/Qwen3-4B-SFT-Fable5",
"longtermrisk/Qwen3-8B-old-bird-names-sft",
"vimleshiit4463/wyzer-2.0-smollm2-135m",
"sashaboguraev/pythia-1b-ppt-shuffle_dyck_steps250_1b-seed208-preserve_emb",
"sashaboguraev/pythia-1b-ppt-random_numbers_steps100_1b-seed208",
"flavianv/deepoutfit-qwen17b-sft-dpo",
"sashaboguraev/pythia-1b-ppt-random_numbers_steps250_1b-seed324-preserve_emb",
"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed1024-preserve_emb",
"longtermrisk/Qwen3-8B-bad-medical-full",
"Siddh07ETH/Pluto-Genesis-0.6B",
"tomhu/RL4TG-Qwen2.5-3B-OPD-7B-Teacher",
"ayushshah/Qwen3-1.7B-UltraChat-SFT",
"tomhu/RL4TG-Qwen2.5-3B-GRPO-2-Epochs",
"huggingFacing/qwen2.5-7b-to-1.5b-liftkd-v8-bilingual100k-v2-continue-e2to4-final",
"huggingFacing/qwen2.5-7b-to-1.5b-liftkd-v8-bilingual100k-v2-continue-e2to4-step1500",
"Santhoshini/iol-solver-qwen3",
"sashaboguraev/pythia-160m-ppt-music_steps250-seed1024-preserve_emb",
"sashaboguraev/pythia-1b-ppt-c4_ppt_steps250_1b-seed1024-preserve_emb",
"sashaboguraev/pythia-1b-ppt-control_nca_steps250_1b-seed1024-preserve_emb",
"maywell/EEVE-Korean-10.8B-v1.0-16k",
"sashaboguraev/pythia-160m-ppt-random_numbers_steps250-seed324",
]
MTHREADS_MODELS = [
# 暂未在本轮提交，留空占位；config_content 已在 build_config_content 中就绪，未来可直接填充列表并加入 GPU_JOBS
]
# 本轮仅提交 MetaX_c-500 / hygon_k100-ai / Cambricon_mlu-370-x8 这 3 张卡
# Kunlunxin_p-800（筛选结果为0，暂无可提交模型）/ Biren_166m（本轮不提交，保留代码与既有列表）/
# Mthreads_s4000（config_content 已就绪，本轮不提交）均不列入本次 GPU_JOBS
GPU_JOBS: List[Tuple[str, List[str]]] = [
("MetaX_c-500", METAX_MODELS),
("hygon_k100-ai", HYGON_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 == "MetaX_c-500":
return f"""
docker_image: git.modelhub.org.cn:9443/enginex-metax/vllm:0.9.1
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
framework: vllm
lang: en
storage: gpfs
api: chat
max_model_len: 4096
sut_config:
gpu_num: 1
values:
command: ['/opt/conda/bin/vllm', 'serve', '/model', '--port', '20644', '--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 == "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"]
"""
elif gpu_type == "hygon_k100-ai":
return f"""
docker_image: harbor.4pd.io/modelhubxc/enginex-hygon/vllm:0.9.2-patch-tokenizer
nv_docker_image: harbor.4pd.io/modelhubxc/enginex-nvidia/vllm:0.11.0-patch-tokenizer
framework: vllm
storage: gpfs
max_model_len: 4096
sut_config:
gpu_num: 1
values:
command: ['vllm', 'serve', '/model', '--port', '20644', '--served-model-name', 'llm', '--max-model-len', '4096', '--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 == "Mthreads_s4000":
return f"""
docker_image: git.modelhub.org.cn:9443/enginex-mthreads/vllm-musa-qy2-py310:v0.8.4-release
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
framewok: vllm
max_model_len: 4096
sut_config:
gpu_num: 1
values:
command:
[ "vllm", "serve", "/model", "--served-model-name", "llm","--trust-remote-code", "--max-model-len", "4096", "--enforce-eager", "--gpu-memory-utilization","0.5"]
ref_config:
gpu_num: 1
values:
command:
[ "vllm","serve", "/model", "--served-model-name", "llm", "--trust-remote-code", "--max-model-len", "4096", "--enforce-eager" ]
"""
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()
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