feat: 添加Docker部署配置
Dockerfile: 多阶段构建, CPU版PyTorch, 模型预加载 docker-compose: 健康检查, 数据卷持久化, 环境变量注入 requirements.lock.txt: 锁定依赖版本确保构建可复现
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.dockerignore
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.dockerignore
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venv/
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__pycache__/
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*.pyc
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.git/
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.env
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data/
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docs/
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*.md
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.gitignore
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.env.example
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.pytest_cache/
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Dockerfile
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Dockerfile
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# ── Stage 1: 依赖安装 ──────────────────────────────────────
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FROM python:3.12-slim AS builder
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WORKDIR /build
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# 先拷贝依赖文件,利用 Docker 缓存
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COPY requirements.lock.txt .
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RUN pip install --no-cache-dir -r requirements.lock.txt
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# ── Stage 2: 运行时 ────────────────────────────────────────
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FROM python:3.12-slim
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LABEL maintainer="LLM Compass"
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LABEL description="智能LLM路由服务,为请求指引最优模型"
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WORKDIR /app
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# 安装运行时系统依赖(sentencepiece 等)
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RUN apt-get update && \
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apt-get install -y --no-install-recommends libgomp1 && \
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rm -rf /var/lib/apt/lists/*
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# 从 builder 拷贝 Python 包
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COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
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COPY --from=builder /usr/local/bin /usr/local/bin
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# 拷贝应用代码
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COPY config.py main.py nvidia_router.py ./
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# 创建数据目录
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RUN mkdir -p /app/data
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# 预下载 NVIDIA 模型(构建时缓存,避免每次启动下载)
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RUN python -c "from nvidia_router import get_nvidia_router; r = get_nvidia_router(); r.initialize(); print('Model preloaded successfully')" || echo "Model preload failed, will download on first request"
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# 环境变量(敏感信息通过 docker-compose / --env-file 注入)
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ENV PYTHONUNBUFFERED=1
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# 暴露端口
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EXPOSE 8000
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# 数据持久化
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VOLUME ["/app/data"]
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# 启动命令
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"]
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docker-compose.yml
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docker-compose.yml
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services:
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llm-compass:
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build: .
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container_name: llm-compass
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ports:
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- "8000:8000"
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environment:
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- DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY}
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volumes:
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- compass-data:/app/data
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restart: unless-stopped
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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volumes:
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compass-data:
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requirements.lock.txt
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requirements.lock.txt
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# LLM Compass - Docker 锁定依赖 (CPU)
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# 使用 CPU 版 PyTorch,大幅减小镜像体积
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--extra-index-url https://download.pytorch.org/whl/cpu
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fastapi==0.136.0
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uvicorn[standard]==0.44.0
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pydantic==2.12.5
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litellm==1.83.9
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tiktoken==0.12.0
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httpx==0.28.1
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python-dotenv==1.0.1
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torch==2.2.2+cpu
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transformers==4.57.6
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tokenizers==0.22.2
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safetensors==0.4.3
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numpy==1.26.4
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sentencepiece==0.2.1
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huggingface_hub>=0.28.0
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