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Dockerfile
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FROM modelhubxc-4pd.tencentcloudcr.com/xc_agent_platform/python:3.11-slim
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ENV PYTHONUNBUFFERED=1
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
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COPY . .
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EXPOSE 8080
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CMD ["python", "main.py"]
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main.py
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main.py
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import requests
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import json
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from typing import List, Tuple
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# ========== 全局配置 ==========
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BASE_URL = "https://modelhub.org.cn"
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LOGIN_ENDPOINT = "/adminApi/user/login"
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SUBMIT_TEST_TASK_ENDPOINT = "/adminApi/async/task/create-contest-task"
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USER_ACCOUNT = "zhoushasha@4paradigm.com"
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USER_PASSWORD = "4pdpassword"
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CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
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CONTRIBUTORS = "zhoushasha"
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GPU_TYPE = "Cambricon_mlu-370-x8"
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TASK_TYPE = "text-generation"
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HEADERS = {"Content-Type": "application/json"}
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# ======== 模型列表(保持不变)========
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ALL_MODEL_IDS = [
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"AI-ModelScope/gemma-2b",
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"AI-ModelScope/falcon-mamba-7b",
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"katanemo/deepseek-2",
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"OpenBMB/MiniCPM4-0.5B",
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"NousResearch/Meta-Llama-3-8B-Instruct",
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"MediaTek-Research/Breeze-7B-Instruct-v1_0",
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"QLUNLP/BianCang-Qwen2.5-7B-Instruct",
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"OpenBMB/MiniCPM4-Survey",
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"OpenBMB/MiniCPM4-8B",
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"PaddlePaddle/ERNIE-4.5-0.3B-PT",
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"LLM-Research/Llama-Guard-3-8B",
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"OpenBMB/MiniCPM-2B-dpo-fp16",
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"OpenBMB/MiniCPM4.1-8B",
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"Cylingo/Xinyuan-LLM-14B-0428",
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"Fengshenbang/Ziya-LLaMA-13B-v1",
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"baichuan-inc/Baichuan2-13B-Chat",
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"LLM-Research/gemma-2-9b-it",
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"Qwen/CodeQwen1.5-7B-Chat",
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"OpenBMB/cpm-bee-10b",
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"OpenBMB/MiniCPM3-4B",
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]
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# === 登录获取 token ===
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def login():
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payload = {"userAccount": USER_ACCOUNT, "userPassword": USER_PASSWORD}
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print("🔑 正在登录...")
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resp = requests.post(BASE_URL + LOGIN_ENDPOINT, headers=HEADERS, json=payload)
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if resp.status_code != 200:
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raise Exception(f"HTTP 登录失败: {resp.text}")
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data = resp.json()
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if data.get("code") != 0:
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raise Exception(f"业务登录失败: {data.get('message')}")
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token = data["data"]["token"]
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print("✅ 登录成功!")
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return token
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# === 提交单个模型的测试任务(vLLM + kunlunxin_p-800)===
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def submit_test_task(token: str, model_id: str) -> Tuple[str, str]:
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auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"}
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config_content = f"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
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nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
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framework: vllm
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storage: gpfs
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modelhub_options:
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srcRelativePath: leaderboard/modelHubXC/{model_id}
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mountPoint: /model
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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: MAX_MODEL_LEN
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value: 8192
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command: ["vllm", "serve", "/model", "--port", "8000", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
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ref_config:
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values:
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cpu_num: 2
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gpu_num: 1
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env:
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- name: MAX_MODEL_LEN
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value: 8192
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command: ["vllm", "serve", "/model", "--port", "80", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
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"""
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task_data = {
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"contestApiToken": CONTEST_API_TOKEN,
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"contributors": CONTRIBUTORS,
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"gpuTypes": [GPU_TYPE],
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"taskType": TASK_TYPE,
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"modelId": model_id,
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"framework": "vllm",
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"submissionConfig": [{
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"config": config_content,
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"gpuType": GPU_TYPE,
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"taskType": TASK_TYPE
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}]
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}
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print(f"📤 提交验证任务: {model_id} (GPU: {GPU_TYPE})")
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try:
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resp = requests.post(BASE_URL + SUBMIT_TEST_TASK_ENDPOINT, json=task_data, headers=auth_headers, timeout=15)
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if resp.status_code == 200:
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result = resp.json()
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if result.get("code") == 0:
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task_id = result.get("data", {}).get("id")
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print(f"✅ 验证任务提交成功! Task ID: {task_id}")
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return task_id, model_id
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else:
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print(f"❌ 验证任务业务错误 ({model_id}): {result.get('message')}")
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return None, model_id
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else:
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print(f"❌ 验证任务 HTTP 错误 ({model_id}): {resp.status_code} - {resp.text}")
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return None, model_id
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except Exception as e:
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print(f"💥 提交验证任务异常 ({model_id}): {e}")
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return None, model_id
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# === 主函数:仅提交验证任务 ===
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def main():
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if not ALL_MODEL_IDS:
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print("❌ 模型列表为空,请在 ALL_MODEL_IDS 中填入模型ID")
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return
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token = login()
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total_count = len(ALL_MODEL_IDS)
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print(f"📊 共 {total_count} 个模型待提交验证任务")
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successful_tasks: List[Tuple[str, str]] = [] # (task_id, model_id)
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for model_id in ALL_MODEL_IDS:
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task_id, mid = submit_test_task(token, model_id)
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if task_id:
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successful_tasks.append((task_id, mid))
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# 写入成功提交的 task_id 和 model_id 到文件
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with open("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
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for tid, mid in successful_tasks:
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f.write(f"{tid}\t{mid}\n")
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# 最终统计
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print("\n" + "=" * 60)
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print(f"🎉 全部完成!")
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print(f"✅ 成功提交验证任务: {len(successful_tasks)}")
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print(f"📄 详情已写入: submitted_validation_tasks.txt")
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print(f"📊 总计尝试: {total_count}")
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if __name__ == "__main__":
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main()
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