test: 测试GitHub构建docker
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name: Build and Push Multi-Arch Docker Image
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on:
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push:
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branches:
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- main
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- master
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tags:
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- 'v*'
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pull_request:
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branches:
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- main
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- master
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workflow_dispatch: # 允许手动触发
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env:
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DOCKER_IMAGE: mumujie/mumuainovel
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jobs:
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build-and-push:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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with:
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platforms: linux/amd64,linux/arm64
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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with:
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driver-opts: |
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image=moby/buildkit:master
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network=host
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- name: Login to Docker Hub
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if: github.event_name != 'pull_request'
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uses: docker/login-action@v3
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with:
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Extract metadata (tags, labels)
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.DOCKER_IMAGE }}
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tags: |
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# 对于main/master分支,打latest标签
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type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master' }}
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# 对于tag,使用版本号
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type=semver,pattern={{version}}
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type=semver,pattern={{major}}.{{minor}}
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type=semver,pattern={{major}}
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# 使用分支名
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type=ref,event=branch
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# 使用commit SHA
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type=sha,prefix=sha-
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- name: Build and push Docker image
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uses: docker/build-push-action@v5
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with:
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context: .
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file: ./Dockerfile
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platforms: linux/amd64,linux/arm64
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push: ${{ github.event_name != 'pull_request' }}
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha
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cache-to: type=gha,mode=max
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build-args: |
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BUILDKIT_INLINE_CACHE=1
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- name: Update Docker Hub Description
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if: github.event_name != 'pull_request' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master')
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uses: peter-evans/dockerhub-description@v4
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with:
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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repository: ${{ env.DOCKER_IMAGE }}
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readme-filepath: ./README.md
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continue-on-error: true
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+54
-13
@@ -1,16 +1,25 @@
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# 多阶段构建 Dockerfile for AI Story Creator
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# 支持多架构构建: linux/amd64, linux/arm64
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# 构建参数
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ARG USE_CN_MIRROR=false
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# 阶段1: 构建前端
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FROM node:22-alpine AS frontend-builder
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ARG USE_CN_MIRROR
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WORKDIR /frontend
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# 复制前端依赖文件
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COPY frontend/package*.json ./
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# 使用国内npm镜像加速
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RUN npm config set registry https://registry.npmmirror.com
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# 根据参数决定是否使用国内npm镜像
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RUN if [ "$USE_CN_MIRROR" = "true" ]; then \
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npm config set registry https://registry.npmmirror.com; \
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fi
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# 删除 package-lock.json 以SC避免因镜像源不一致导致的 404 错误
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# 删除 package-lock.json 以避免因镜像源不一致导致的 404 错误
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RUN rm -f package-lock.json
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# 安装依赖
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@@ -28,12 +37,18 @@ 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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ARG USE_CN_MIRROR
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ARG TARGETPLATFORM
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ARG TARGETARCH
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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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# 根据参数决定是否使用国内镜像源
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RUN if [ "$USE_CN_MIRROR" = "true" ]; then \
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sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources && \
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sed -i 's/security.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources; \
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fi
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# 安装系统依赖(添加数据库工具)
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RUN apt-get update && apt-get install -y \
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@@ -45,13 +60,40 @@ RUN apt-get update && apt-get install -y \
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# 复制后端依赖文件
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COPY backend/requirements.txt ./
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# 先从PyTorch官方源安装CPU版本的torch(避免GPU依赖)
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RUN pip install --no-cache-dir torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu
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# 根据架构安装PyTorch CPU版本
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# arm64架构使用pip直接安装,amd64使用PyTorch官方CPU源
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RUN if [ "$TARGETARCH" = "arm64" ]; then \
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pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu || \
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pip install --no-cache-dir torch; \
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else \
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pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu; \
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fi
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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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# 安装其他Python依赖
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RUN if [ "$USE_CN_MIRROR" = "true" ]; then \
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pip install --no-cache-dir -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/; \
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else \
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pip install --no-cache-dir -r requirements.txt; \
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fi
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# 复制后端代码(包含embedding模型)
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# 创建embedding目录
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RUN mkdir -p /app/embedding
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# 设置 Sentence-Transformers 缓存目录
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ENV SENTENCE_TRANSFORMERS_HOME=/app/embedding
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# 下载 embedding 模型(从 HuggingFace)
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# 使用 Python 脚本预下载模型,这样运行时不需要网络
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RUN python -c "\
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from sentence_transformers import SentenceTransformer; \
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import os; \
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os.environ['SENTENCE_TRANSFORMERS_HOME'] = '/app/embedding'; \
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print('Downloading sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2...'); \
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model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'); \
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print('Model downloaded successfully!'); \
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"
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# 复制后端代码(不包含embedding,因为已经下载了)
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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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@@ -77,11 +119,10 @@ 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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# 设置运行时为离线模式(模型已在构建时下载)
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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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