""" xc_validation_strategy_gguf_hygon — 主入口 GGUF 模型下载 + 验证任务提交流水线（部署框架与 xc_validation_strategy 一致）。 启动后运行流水线：批量创建 GGUF 模型下载任务（最大并发 8， 遇到并发限制自动等待重试），每个模型下载成功后立即提交 hygon / bi150 两个验证任务； 同时暴露 /health（K8s 探活）和 /status（运行状态）。 """ import json import os import re import signal import threading from datetime import datetime from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from typing import Set import requests # ══════════════════════════════════════════════════════════ # 配置 # ══════════════════════════════════════════════════════════ BASE_URL = os.environ.get("BASE_URL", "https://modelhub.org.cn") LOGIN_ENDPOINT = "/adminApi/user/login" CREATE_DOWNLOAD_TASK_ENDPOINT = "/adminApi/async/task/model-download-task" SUBMIT_TEST_TASK_ENDPOINT = "/adminApi/async/task/create-contest-task" # 流水线内部登录账号（创建下载任务 / 提交验证任务用） USER_ACCOUNT = "zhangyuanxi" USER_PASSWORD = "jianghanjiao0" CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d" HF_TOKEN = "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" CONTRIBUTORS = "zhoushasha" TASK_TYPE = "text-generation" STRATEGY_ID = os.environ.get("STRATEGY_ID", "") # 平台自动注入，无需修改 MAX_CONCURRENT_DOWNLOADS = 8 # 同时下载的模型数上限 CHECK_INTERVAL_SECONDS = 10 # 下载状态轮询间隔（秒） RETRY_WAIT_SECONDS = 20 # 命中并发限制时的等待时间（秒） HTTP_HOST = "0.0.0.0" HTTP_PORT = 8080 # ══════════════════════════════════════════════════════════ # 模型列表 # ══════════════════════════════════════════════════════════ ALL_MODEL_IDS = [ "mradermacher/Ice0.80-03.02-RP-GGUF", "mradermacher/NeuralLemonKuno-GGUF", "mradermacher/DavidAU-Dark_Mistress-8B-GGUF", "mradermacher/dermai-v2-GGUF", "mradermacher/LemonNalyvkaRP-GGUF", "mradermacher/distill_70b_infra_together-GGUF", "mradermacher/nsfw-i-hate-my-life-v1-GGUF", "mradermacher/M7Yamshadowexperiment28_Ognoexperiment27Multi_verse_model-GGUF", "mradermacher/nsfw-i-hate-my-life-v2-GGUF", "mradermacher/Experiment28T3q_YamOgno-GGUF", "mradermacher/Ice0.81-03.02-RP-GGUF", "mradermacher/Qwen2-7B-sft-hhrlhf-spin-GGUF", "mradermacher/WillowGPT-GGUF", "mradermacher/Dark_Master-v0.1-8B-GGUF", "mradermacher/YarnGPTII-GGUF", "mradermacher/kyara-lite-sft-1M-GGUF", "mradermacher/text-arco-GGUF", "mradermacher/Mistral-7B-v0.3-sft-ultrachat-GGUF", "mradermacher/Saba2-Preview-GGUF", "mradermacher/Anemoi-4B-GGUF", "mradermacher/exaone-3.5-2.4b-instruct-dacon-llm-GGUF", "mradermacher/light-3B-beta-GGUF", "mradermacher/bellman-mistral-7b-instruct-v0.3-GGUF", "mradermacher/Kosmos-EVAA-Franken-Immersive-v40-8B-GGUF", "mradermacher/Qwen2.5-Interpreter-GGUF", "mradermacher/Llama-3.1-8B-Gemma-2-9B-mix-GGUF", "mradermacher/Llama-3.1-8B-sft-ultrachat-GGUF", "mradermacher/t5-base-english-japanese-GGUF", "mradermacher/tourist-GGUF", "mradermacher/t5-base-japanese-question-generation-GGUF", "mradermacher/t5-qiita-title-generation-GGUF", "mradermacher/Not_Even_My_Final_Form-8B-Model_Stock-GGUF", "mradermacher/Morphing-8B-Model_Stock-GGUF", "mradermacher/Llamaverse-3.1-8B-Instruct-GGUF", "mradermacher/Gemma-2-9B-sft-ultrachat-GGUF", "mradermacher/Italy-10B-GGUF", "mradermacher/Qwen2.5-7B-sft-ultrachat-safeRLHF-GGUF", "mradermacher/Qwen2-7B-sft-ultrachat-safeRLHF-GGUF", "mradermacher/qwen-2.5-0.5b-unlabeled-pile-GGUF", "mradermacher/HQwen-3B-00-fp16-GGUF", "mradermacher/AI_Pet_code-GGUF", "mradermacher/codellama-7b-betterv-params-c2v-chisel-GGUF", "mradermacher/LongRAG-Qwen2.5-7B-Instruct-GGUF", "mradermacher/granite-3.0-3b-a800m-base-GGUF", "mradermacher/granite-3.0-8b-base-GGUF", "mradermacher/moxin-llm-7b-GGUF", "mradermacher/llama-3.2-3b-merged-base-3K-updated-GGUF", "mradermacher/II-Tulu-8B-SFT-GGUF", "mradermacher/HebQwen-json-2025-GGUF", "mradermacher/WiNGPT-Babel-GGUF", "mradermacher/oh-dcft-v3.1-llama-3.1-405b-v2dummytesting-GGUF", "mradermacher/greesychat-turbo-GGUF", "mradermacher/MFANN-abliterated-phi2-merge-unretrained-GGUF", "mradermacher/oh-dcft-v3.1-llama-3.1-405b-qwen-v2dummytesting-GGUF", "mradermacher/Llama-3.1-8B-sft-ultrachat-safeRLHF-GGUF", "mradermacher/Falcon-3B-8k-data-new-classes-GGUF", "mradermacher/Lunar-4B-GGUF", "mradermacher/Zelus-8B-Model_Stock-GGUF", "mradermacher/mpn_mistral7bv3_sft-GGUF", "mradermacher/Qwen2-7B-sft-ultrachat-GGUF", "mradermacher/Llama-3.1-8B-Instruct-GSM8k-GGUF", "mradermacher/Mistral-7B-v0.3-sft-ultrachat-safeRLHF-GGUF", "mradermacher/QwENDEAVR-VL-2B-GGUF", "mradermacher/EXAONE-3.5-7.8B-it-DACON-LLM-GGUF", "mradermacher/Gemma-2-9B-sft-ultrachat-safeRLHF-GGUF", "mradermacher/Eunoia-GwQ-Gemma-9B-GGUF", "mradermacher/periquito-3B-GGUF", "mradermacher/T3qm7xNeuralsirkrishna_Strangemerges_30Ogno-GGUF", "mradermacher/Chalice1-GGUF", "mradermacher/Yamshadowexperiment28M70.5-0.45-0.64-0.12-0.94-0.72-7B-GGUF", "mradermacher/M7Yamshadowexperiment28_Strangemerges_32Yamshadow-GGUF", "mradermacher/Experiment26Yam_Strangemerges_32Ogno-GGUF", "mradermacher/Experiment28T3q_NeuralsirkrishnaExperiment26-GGUF", "mradermacher/YamshadowStrangemerges_32_Strangemerges_32Experiment28-GGUF", "mradermacher/Experiment28M7_Experiment28Neuralsirkrishna-GGUF", "mradermacher/YamshadowStrangemerges_32_Neuralsirkrishnaexperiment26Experiment24-GGUF", "mradermacher/YamshadowInex12_Experiment29Pastiche-GGUF", "mradermacher/tsukasa-7b-qlora-GGUF", "mradermacher/Qwen2.5-3B-Model-Stock-v2-GGUF", "mradermacher/LongRAG_Ministral-8B-Instruct-GGUF", "mradermacher/LongRAG_Llama3.1-8B-Instruct-GGUF", "mradermacher/inter-play-sim-assistant-test-GGUF", "mradermacher/Llama-3.2-1B-magnitude-0.1-GGUF", "mradermacher/Howdy-8B-LINEAR-GGUF", "mradermacher/Qwen2.5-3B-Model-Stock-GGUF", "mradermacher/Ice0.51-16.01-RP-GGUF", "mradermacher/Qwen2.5-3B-Model-Stock-v3-GGUF", "mradermacher/SalvadAI-GGUF", "mradermacher/Yi-6B-Chat-Llama-3.3-70B-Instruct-honest_lying-GGUF", "mradermacher/Funny-Chatbot-Llama-3.1-8B-Instruct-GGUF", "mradermacher/Aira-2-355M-GGUF", "mradermacher/Qmerft-GGUF", "mradermacher/Qwen2.5-1.5B-Instruct-DPO-bad-boy-GGUF", "mradermacher/NxMobileLM-1.5B-SFT-GGUF", "mradermacher/Llama-3-ELYZA-JP-8B-normal-chosen-GGUF", "mradermacher/HebQwen-json-2025-meta-GGUF", "mradermacher/JapMed-GGUF", "mradermacher/What_A_Thrill-8B-Model_Stock-GGUF", "mradermacher/phi-3-clinical-GGUF", "mradermacher/ORPO-Llama-SQL-8B-Spider-GGUF", "mradermacher/Gemma-Radiation-RP-9B-lorablated-GGUF", "mradermacher/D.A.T.A.v1-GGUF", "mradermacher/gemmarpp-GGUF", "mradermacher/Meta-Llama-3-8B-wanda-0.1-GGUF", "mradermacher/LwQ-10B-Instruct-GGUF", "mradermacher/occiglot-7b-de-en-instruct-dpo1-GGUF", "mradermacher/MCQ-qkvo-2-GGUF", "mradermacher/YamshadowStrangemerges_32_Inex12Experiment28-GGUF", "mradermacher/Bahasa-4b-GGUF", "mradermacher/MCQ-head-16-GGUF", "mradermacher/Experiment26Yam_Ognoexperiment27Multi_verse_model-GGUF", "mradermacher/An4-7Bv2.4-GGUF", "mradermacher/YamshadowStrangemerges_32_YamExperiment28-GGUF", "mradermacher/bitmap-M7-alpaca-70m-GGUF", "mradermacher/UpshotLlama-3-8B-GGUF", "mradermacher/ChimeraLlama-3-8B-GGUF", "mradermacher/Yamshadowexperiment28M70.8-0.85-0.86-0.36-0.42-0.16-7B-GGUF", "mradermacher/microcoderfim-1B-GGUF", "mradermacher/Medusa_OpenHermes-2.5-Mistral7B-DPO-GGUF", "mradermacher/YamshadowInex12_Experiment24Ognoexperiment27-GGUF", "mradermacher/gemma-2b-datascience-instruct-v4.5-GGUF", "mradermacher/Josiefied-Qwen2-1.5B-Instruct-abliterated-GGUF", "mradermacher/zoyllm-7b-slimorca-GGUF", "mradermacher/M7Yamshadowexperiment28_T3qm7xExperiment28-GGUF", "mradermacher/Aria-7b-128k-v4-GGUF", "mradermacher/T3qm7xNeuralsirkrishna_CalmeInex12-GGUF", "mradermacher/T3qm7xNeuralsirkrishna_YamStrangemerges_32-GGUF", "mradermacher/YamshadowInex12_T3qm7xExperiment28-GGUF", "mradermacher/Model_Stock_Mixv0.1-7B-GGUF", "mradermacher/StrangeMerges_49-7B-dare_ties-GGUF", "mradermacher/galland-mistral-7b-instruct-v0.2-v1-GGUF", "mradermacher/Magiq-Translator-0-GGUF", "mradermacher/occiglot-7b-de-en-instruct-GGUF", "mradermacher/M7Yamshadowexperiment28_YamShadow-GGUF", "mradermacher/StrangeMerges_17-7B-dare_ties-GGUF", "mradermacher/occiglot-7b-es-en-instruct-hf-GGUF", "mradermacher/ko-gemma-1.1-2b-GGUF", "mradermacher/M7Yamshadowexperiment28_Percival_01Yamshadow-GGUF", "mradermacher/TinyAlpaca-1.1B-GGUF", "mradermacher/MeliodasT3q_M7Alloyingotneoy-GGUF", "mradermacher/Vistral-7B-Chat-function-calling-GGUF", ] # 去重（保持原有顺序） _seen = set() _deduplicated = [] for _mid in ALL_MODEL_IDS: if _mid not in _seen: _deduplicated.append(_mid) _seen.add(_mid) ALL_MODEL_IDS = _deduplicated print(f"[INFO] 去重后模型数量: {len(ALL_MODEL_IDS)}", flush=True) HEADERS = {"Content-Type": "application/json"} # ══════════════════════════════════════════════════════════ # 全局状态（供 /status 展示） # ══════════════════════════════════════════════════════════ _state = { "strategy_id": STRATEGY_ID, "phase": "starting", # starting | running | done | error "total": len(ALL_MODEL_IDS), "downloading": [], # 当前正在下载的模型 "download_success": 0, "download_failed": 0, "submitted": 0, # 成功提交的验证任务数（hygon + bi150） "submit_failed": 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) # ══════════════════════════════════════════════════════════ # 工具函数：生成模型文件名 # ══════════════════════════════════════════════════════════ def get_model_filename(model_id: str) -> str: """ 从 model_id 生成标准 GGUF 模型文件名。 规则： - 移除组织名（/ 前部分） - 处理 '_-_' 分割（保留原有逻辑） - 移除末尾 '-GGUF'（不区分大小写） - 若移除后以 -i1, -i2, ..., -i99 结尾： → 替换为 .i1, .i2, ... 并添加 '-Q4_0.gguf' 否则： → 直接添加 '.f16.gguf' """ base_name = model_id.split("/")[-1] if '_-_' in base_name: base_name = base_name.split('_-_')[-1] if base_name.lower().endswith("-gguf"): base_name = base_name[:-5] match = re.search(r'-i(\d+)$', base_name) if match: number = match.group(1) base_name = base_name[:match.start()] + f".i{number}-Q4_0.gguf" else: base_name = base_name + ".f16.gguf" return base_name # ══════════════════════════════════════════════════════════ # 登录获取 token # ══════════════════════════════════════════════════════════ def login(account: str = USER_ACCOUNT, password: str = USER_PASSWORD) -> str: payload = {"userAccount": account, "userPassword": password} print(f"🔑 正在登录: {account} ...", flush=True) resp = requests.post(BASE_URL + LOGIN_ENDPOINT, headers=HEADERS, json=payload, timeout=15) if resp.status_code != 200: raise Exception(f"HTTP 登录失败: {resp.text}") data = resp.json() if data.get("code") != 0: raise Exception(f"业务登录失败: {data.get('message')}") token = data["data"]["token"] print(f"✅ 登录成功: {account}", flush=True) return token # ══════════════════════════════════════════════════════════ # 创建单个模型的下载任务（只发一次请求，由外部控制重试） # ══════════════════════════════════════════════════════════ def create_download_task_once(token: str, model_id: str): filename = get_model_filename(model_id) auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"} payload = { "allowPatterns": [filename], "hfToken": HF_TOKEN, "modelId": model_id, "source": "HUGGING_FACE", "stillDownloadAlreadySuccessDownloadedModel": False } print(f"📥 创建下载任务: {model_id} → {filename}", flush=True) try: resp = requests.post(BASE_URL + CREATE_DOWNLOAD_TASK_ENDPOINT, headers=auth_headers, json=payload, timeout=15) if resp.status_code == 200: data = resp.json() if data.get("code") == 0: print(f"✅ 下载任务已提交: {model_id}", flush=True) return True, "SUCCESS" else: message = data.get("message", "Unknown Error") print(f"⚠️ 下载任务业务失败 ({model_id}): {message}", flush=True) return False, message else: print(f"❌ HTTP 错误 ({model_id}): {resp.status_code} - {resp.text}", flush=True) return False, "HTTP_ERROR" except Exception as e: print(f"💥 创建下载任务异常 ({model_id}): {e}", flush=True) return False, "EXCEPTION" # ══════════════════════════════════════════════════════════ # 查询单个模型的最新下载任务状态 # ══════════════════════════════════════════════════════════ def check_model_status(token: str, model_id: str) -> str: """ 返回状态: 'WAITING', 'RUNNING', 'SUCCESS', 'FAILED', 'UNKNOWN' """ url = BASE_URL + CREATE_DOWNLOAD_TASK_ENDPOINT auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"} params = {"modelId": model_id, "current": 1, "pageSize": 1} try: resp = requests.get(url, headers=auth_headers, params=params, timeout=10) if resp.status_code != 200: return "UNKNOWN" data = resp.json() if data.get("code") != 0: return "UNKNOWN" records = data.get("data", {}).get("records", []) if not records: return "UNKNOWN" status = records[0].get("status", "UNKNOWN").upper() return status except Exception as e: print(f"⚠️ 查询状态异常 ({model_id}): {e}", flush=True) return "UNKNOWN" # ══════════════════════════════════════════════════════════ # 提交单个模型的测试任务 hygon # ══════════════════════════════════════════════════════════ def submit_test_task(token: str, model_id: str) -> bool: auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"} model_filename = get_model_filename(model_id) gpu_type = "hygon_k100-ai" config_content = f"""docker_image: git.modelhub.org.cn:9443/enginex-hygon/hygon-llama.cpp:b7516 nv_docker_image: harbor-contest.4pd.io/luxinlong02/llama-cpp:b7003-cuda-full-12.3 framework: llamacpp storage: gpfs modelhub_options: srcRelativePath: leaderboard/modelHubXC/{model_id} mountPoint: /model api: completion temperature: 0 repetition_penalty: 1.1 top_p: 0.9 max_model_len: 4096 sut_config: gpu_num: 1 values: command: ['/app/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20','--n-gpu-layers', '999', '--prio', '3', '--min_p', '0.01', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off'] ref_config: gpu_num: 1 values: command: ['/workspace/llama.cpp/build/bin/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20', '--n-gpu-layers', '999', '--prio', '3', '--min_p', '0.01', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off'] """ task_data = { "contestApiToken": CONTEST_API_TOKEN, "contributors": CONTRIBUTORS, "gpuTypes": [gpu_type], "taskType": TASK_TYPE, "modelId": model_id, "strategyId": STRATEGY_ID, # 平台要求 "submissionConfig": [{ "config": config_content, "gpuType": gpu_type, "taskType": TASK_TYPE }] } print(f"📤 提交测试任务 (hygon): {model_id}", flush=True) try: resp = requests.post(BASE_URL + SUBMIT_TEST_TASK_ENDPOINT, json=task_data, headers=auth_headers, timeout=15) if resp.status_code == 200: result = resp.json() if result.get("code") == 0: task_id = result.get("data", {}).get("taskId") print(f"✅ 测试任务提交成功! Task ID: {task_id}", flush=True) return True else: print(f"❌ 测试任务业务错误: {result.get('message')}", flush=True) return False else: print(f"❌ 测试任务 HTTP 错误: {resp.status_code} - {resp.text}", flush=True) return False except Exception as e: print(f"💥 提交测试任务异常 ({model_id}): {e}", flush=True) return False # ══════════════════════════════════════════════════════════ # 提交单个模型的测试任务 bi150 # ══════════════════════════════════════════════════════════ def submit_test_task_bi150(token: str, model_id: str) -> bool: auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"} model_filename = get_model_filename(model_id) gpu_type = "Iluvatar_bi-150" config_content = f"""docker_image: git.modelhub.org.cn:9443/enginex-iluvatar/iluvatar-llama.cpp:b7516-bi150 nv_docker_image: harbor-contest.4pd.io/luxinlong02/llama-cpp:b7003-cuda-full-12.3 framework: llamacpp storage: gpfs modelhub_options: srcRelativePath: leaderboard/modelHubXC/{model_id} mountPoint: /model max_model_len: 4096 sut_config: gpu_num: 1 values: command: ['/app/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20','--n-gpu-layers','128', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off', '--no-mmap', '--sync-to-temp'] ref_config: gpu_num: 1 values: command: ['/workspace/llama.cpp/build/bin/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20','--n-gpu-layers','128', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off'] """ task_data = { "contestApiToken": CONTEST_API_TOKEN, "contributors": CONTRIBUTORS, "gpuTypes": [gpu_type], "taskType": TASK_TYPE, "modelId": model_id, "strategyId": STRATEGY_ID, # 平台要求 "submissionConfig": [{ "config": config_content, "gpuType": gpu_type, "taskType": TASK_TYPE }] } print(f"📤 提交测试任务 (bi150): {model_id}", flush=True) try: resp = requests.post(BASE_URL + SUBMIT_TEST_TASK_ENDPOINT, json=task_data, headers=auth_headers, timeout=15) if resp.status_code == 200: result = resp.json() if result.get("code") == 0: task_id = result.get("data", {}).get("taskId") print(f"✅ 测试任务提交成功! Task ID: {task_id}", flush=True) return True else: print(f"❌ 测试任务业务错误: {result.get('message')}", flush=True) return False else: print(f"❌ 测试任务 HTTP 错误: {resp.status_code} - {resp.text}", flush=True) return False except Exception as e: print(f"💥 提交测试任务异常 ({model_id}): {e}", flush=True) return False # ══════════════════════════════════════════════════════════ # 业务逻辑：动态流水线（下载 → 提交验证任务，带并发限制重试） # ══════════════════════════════════════════════════════════ def _run_worker(): _state["started_at"] = datetime.utcnow().isoformat() _state["phase"] = "running" try: token = login() except Exception as e: print(f"[worker] 登录失败: {e}", flush=True) _state["phase"] = "error" return pending_models = list(ALL_MODEL_IDS) # 尚未开始下载的模型 active_models: Set[str] = set() # 当前正在下载的模型 completed_results = {} # model_id -> status print(f"🚀 总共 {len(pending_models)} 个模型待下载。最大并发数: {MAX_CONCURRENT_DOWNLOADS}\n", flush=True) while (pending_models or active_models) and not _shutdown.is_set(): # 1. 检查并更新活跃任务的状态 for model_id in list(active_models): if _shutdown.is_set(): break status = check_model_status(token, model_id) if status in ("SUCCESS", "FAILED"): active_models.remove(model_id) completed_results[model_id] = status print(f"⏹️ {model_id} 完成，状态: {status} | 活跃数: {len(active_models)}", flush=True) if status == "SUCCESS": _state["download_success"] += 1 # 下载成功后立即提交该模型的验证任务 if submit_test_task(token, model_id): _state["submitted"] += 1 print(f"🧪 已为 {model_id} 提交 hygon 验证任务", flush=True) else: _state["submit_failed"] += 1 print(f"⚠️ {model_id} hygon 验证任务提交失败", flush=True) if submit_test_task_bi150(token, model_id): _state["submitted"] += 1 print(f"🧪 已为 {model_id} 提交 bi150 验证任务", flush=True) else: _state["submit_failed"] += 1 print(f"⚠️ {model_id} bi150 验证任务提交失败", flush=True) else: _state["download_failed"] += 1 # 2. 提交新任务（遇到并发限制原地等待重试，其他错误丢弃） while len(active_models) < MAX_CONCURRENT_DOWNLOADS and pending_models and not _shutdown.is_set(): next_model = pending_models[0] # 只看队首，不先弹出 success = False while not success and next_model in pending_models and not _shutdown.is_set(): success, message = create_download_task_once(token, next_model) if success: pending_models.pop(0) active_models.add(next_model) print(f"▶️ 启动下载: {next_model} (当前活跃: {len(active_models)})", flush=True) break else: if "用户最多同时运行 8 个下载任务" in message: print(f"⏳ 检测到并发限制，等待 {RETRY_WAIT_SECONDS} 秒后重试该模型...", flush=True) _shutdown.wait(RETRY_WAIT_SECONDS) else: print(f"❌ 非并发错误，丢弃该模型: {next_model}", flush=True) pending_models.pop(0) completed_results[next_model] = "DISCARDED" _state["download_failed"] += 1 break _state["downloading"] = sorted(active_models) # 3. 稍作等待，避免频繁查询（可被 shutdown 信号打断） if active_models or pending_models: _shutdown.wait(CHECK_INTERVAL_SECONDS) print("\n✅ 所有模型处理完毕！\n", flush=True) # 4. 收集所有成功下载的模型ID并写入结果文件 success_models = [ mid for mid, status in completed_results.items() if status == "SUCCESS" ] try: with open("downloaded_success_models.txt", "w", encoding="utf-8") as f: for mid in success_models: f.write(f"{mid}\n") except Exception: pass print("🎉 下载成功的模型ID列表：", flush=True) print("[", flush=True) for mid in success_models: print(f' "{mid}",', flush=True) print("]", flush=True) _state["downloading"] = [] _state["finished_at"] = datetime.utcnow().isoformat() _state["phase"] = "done" print( f"[worker] 完成 download_success={_state['download_success']} " f"download_failed={_state['download_failed']} " f"submitted={_state['submitted']} submit_failed={_state['submit_failed']}", flush=True, ) # 流水线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 服务线程 http_thread = threading.Thread(target=_run_http, daemon=False) http_thread.start() # 流水线线程 worker_thread = threading.Thread(target=_run_worker, daemon=True) worker_thread.start() # 主线程等待 shutdown _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()