xiamuceer
81 lines
2.3 KiB
Docker
81 lines
2.3 KiB
Docker
# 多阶段构建 Dockerfile for AI Story Creator
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# 阶段1: 构建前端
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FROM node:22-alpine AS frontend-builder
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WORKDIR /frontend
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# 复制前端依赖文件
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COPY frontend/package*.json ./
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# 使用国内npm镜像加速
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RUN npm config set registry https://registry.npmmirror.com
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# 安装依赖
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RUN npm install
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# 复制前端源代码
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COPY frontend/ ./
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# 临时修改vite配置，使其输出到dist目录（而不是../backend/static）
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RUN sed -i "s|outDir: '../backend/static'|outDir: 'dist'|g" vite.config.ts
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# 构建前端
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RUN npm run build
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# 阶段2: 构建最终镜像
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FROM python:3.11-slim
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# 设置工作目录
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WORKDIR /app
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# 使用国内镜像源加速
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RUN sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources \
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&& sed -i 's/security.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources
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# 安装系统依赖
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RUN apt-get update && apt-get install -y \
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gcc \
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&& rm -rf /var/lib/apt/lists/*
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# 复制后端依赖文件
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COPY backend/requirements.txt ./
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# 先从PyTorch官方源安装CPU版本的torch（避免GPU依赖）
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RUN pip install --no-cache-dir torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu
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# 再安装其他Python依赖（使用阿里云镜像加速）
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RUN pip install --no-cache-dir -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
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# 复制后端代码
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COPY backend/ ./
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# 从前端构建阶段复制构建好的静态文件
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COPY --from=frontend-builder /frontend/dist ./static
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# 创建必要的目录
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RUN mkdir -p /app/data /app/logs /app/embedding
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# 复制预下载的Embedding模型到独立目录（避免被docker-compose的data挂载覆盖）
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# 这样可以避免首次运行时联网下载约420MB的模型文件
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COPY backend/embedding /app/embedding
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# 暴露端口
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EXPOSE 8000
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# 设置环境变量
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ENV PYTHONUNBUFFERED=1
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ENV APP_HOST=0.0.0.0
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ENV APP_PORT=8000
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# 设置Transformers和Sentence-Transformers离线模式
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ENV TRANSFORMERS_OFFLINE=1
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ENV HF_DATASETS_OFFLINE=1
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ENV HF_HUB_OFFLINE=1
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ENV SENTENCE_TRANSFORMERS_HOME=/app/embedding
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# 健康检查
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1
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# 启动命令
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CMD ["python", "-m", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] |