optimize-docker-build-cache
关于
This skill provides Docker build optimization techniques using layer caching, multi-stage builds, and BuildKit to significantly reduce build times. It's designed for R, Node.js, and Python projects where repeated dependency installations and large image sizes slow development. Use it when code changes trigger full rebuilds or when CI/CD pipelines become bottlenecks.
快速安装
Claude Code
推荐npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/optimize-docker-build-cache在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Optimize Docker Build Cache
Reduce Docker build times through effective layer caching and build optimization.
When to Use
- Docker builds are slow due to repeated package installations
- Rebuilds reinstall all dependencies on every code change
- Image sizes are unnecessarily large
- CI/CD pipeline builds are a bottleneck
Inputs
- Required: Existing Dockerfile to optimize
- Optional: Target build time improvement
- Optional: Target image size reduction
Procedure
Step 1: Order Layers by Change Frequency
Place least-changing layers first:
# 1. Base image (rarely changes)
FROM rocker/r-ver:4.5.0
# 2. System dependencies (change occasionally)
RUN apt-get update && apt-get install -y \
libcurl4-openssl-dev \
libssl-dev \
&& rm -rf /var/lib/apt/lists/*
# 3. Dependency files only (change when deps change)
COPY renv.lock renv.lock
COPY renv/activate.R renv/activate.R
RUN R -e "renv::restore()"
# 4. Source code (changes frequently)
COPY . .
Key principle: Docker caches each layer. When a layer changes, all subsequent layers are rebuilt. Dependency installation should come before source code copy.
Got: The Dockerfile layers are ordered from least-changing (base image, system deps) to most-changing (source code), with dependency lockfiles copied before the full source.
If fail: If builds still reinstall dependencies on every code change, verify that COPY . . comes after the dependency installation RUN command, not before.
Step 2: Separate Dependency Installation from Code
Bad (rebuilds packages on every code change):
COPY . .
RUN R -e "renv::restore()"
Good (only rebuilds packages when lockfile changes):
COPY renv.lock renv.lock
RUN R -e "renv::restore()"
COPY . .
Same pattern for Node.js:
COPY package.json package-lock.json ./
RUN npm ci
COPY . .
Got: Dependency lockfile (renv.lock, package-lock.json, requirements.txt) is copied and installed in a separate layer before the full source code COPY . ..
If fail: If the lockfile copy fails, ensure the file exists in the build context and is not excluded by .dockerignore.
Step 3: Use Multi-Stage Builds
Separate build dependencies from runtime:
# Build stage - includes dev tools
FROM rocker/r-ver:4.5.0 AS builder
RUN apt-get update && apt-get install -y \
libcurl4-openssl-dev libssl-dev build-essential
COPY renv.lock .
RUN R -e "install.packages('renv'); renv::restore()"
# Runtime stage - minimal image
FROM rocker/r-ver:4.5.0
RUN apt-get update && apt-get install -y \
libcurl4 libssl3 \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /usr/local/lib/R/site-library /usr/local/lib/R/site-library
COPY . /app
WORKDIR /app
CMD ["Rscript", "main.R"]
Got: The Dockerfile has a builder stage with dev tools and a runtime stage with only production dependencies. The final image is significantly smaller than a single-stage build.
If fail: If COPY --from=builder fails to find libraries, verify the install path matches between stages. Use docker build --target builder . to debug the build stage independently.
Step 4: Combine RUN Commands
Each RUN creates a layer. Combine related commands:
Bad (3 layers, apt cache persists):
RUN apt-get update
RUN apt-get install -y curl git
RUN rm -rf /var/lib/apt/lists/*
Good (1 layer, clean cache):
RUN apt-get update && apt-get install -y \
curl \
git \
&& rm -rf /var/lib/apt/lists/*
Got: Related apt-get or package install commands are combined into single RUN instructions, each ending with cache cleanup (rm -rf /var/lib/apt/lists/*).
If fail: If a combined RUN command fails midway, temporarily split it to identify the failing command, then recombine after fixing.
Step 5: Use .dockerignore
Prevent unnecessary files from entering the build context:
.git
.Rproj.user
.Rhistory
.RData
renv/library
renv/cache
node_modules
docs/
*.tar.gz
.env
Got: A .dockerignore file exists in the project root excluding .git, node_modules, renv/library, build artifacts, and environment files. Build context size is noticeably smaller.
If fail: If needed files are missing in the container, check .dockerignore for overly broad patterns. Use docker build verbose output to verify which files are sent to the daemon.
Step 6: Enable BuildKit
DOCKER_BUILDKIT=1 docker build -t myimage .
Or in docker-compose.yml:
services:
app:
build:
context: .
dockerfile: Dockerfile
With COMPOSE_DOCKER_CLI_BUILD=1 and DOCKER_BUILDKIT=1 environment variables.
BuildKit enables:
- Parallel stage builds
- Better cache management
--mount=type=cachefor persistent package caches
Got: Builds run with BuildKit enabled (indicated by #1 [internal] load build definition style output). Multi-stage builds execute stages in parallel where possible.
If fail: If BuildKit is not active, verify the environment variables are exported before the build command. On older Docker versions, upgrade Docker Engine to 18.09+ for BuildKit support.
Step 7: Use Cache Mounts for Package Managers
# R packages with persistent cache
RUN --mount=type=cache,target=/usr/local/lib/R/site-library \
R -e "install.packages('dplyr')"
# npm with persistent cache
RUN --mount=type=cache,target=/root/.npm \
npm ci
Got: Subsequent builds reuse cached packages from the mount, dramatically reducing install times even when the layer is invalidated. Cache persists across builds.
If fail: If --mount=type=cache is not recognized, ensure BuildKit is enabled (DOCKER_BUILDKIT=1). The syntax requires BuildKit and is not supported by the legacy builder.
Validation
- Rebuilds after code-only changes are significantly faster
- Dependency installation layer is cached when lockfile has not changed
-
.dockerignoreexcludes unnecessary files - Image size is reduced compared to unoptimized build
- Multi-stage build (if used) separates build and runtime dependencies
Pitfalls
- Copying all files before installing deps: Invalidates the dependency cache on every code change
- Forgetting
.dockerignore: Large build contexts slow down every build - Too many layers: Each
RUN,COPY,ADDcreates a layer. Combine where logical. - Not cleaning apt cache: Always end apt-get installs with
&& rm -rf /var/lib/apt/lists/* - Platform-specific caches: Cache layers are platform-specific. CI runners may not benefit from local caches.
Related Skills
create-r-dockerfile- initial Dockerfile creationsetup-docker-compose- compose build configurationcontainerize-mcp-server- apply optimizations to MCP server builds
GitHub 仓库
相关推荐技能
content-collections
元Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。
polymarket
元这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。
creating-opencode-plugins
元该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。
sglang
元SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。
