buildkit

buildkit

高效灵活的开源构建工具包

BuildKit是一个高效的开源构建工具包,用于将源代码转换为构建产物。它提供自动垃圾回收、可扩展前端、并发依赖解析、指令缓存、缓存导入导出、嵌套构建等功能。BuildKit支持多种输出格式,采用插件化架构,可在无root权限下执行。它通过LLB中间格式定义依赖图,兼容Dockerfile等多种语言,广泛应用于Docker和Kubernetes等项目中。

BuildKit构建工具容器化缓存多平台构建Github开源项目

asciicinema example

BuildKit <!-- omit in toc -->

GitHub Release PkgGoDev CI BuildKit Status CI Frontend Status Go Report Card Codecov

BuildKit is a toolkit for converting source code to build artifacts in an efficient, expressive and repeatable manner.

Key features:

  • Automatic garbage collection
  • Extendable frontend formats
  • Concurrent dependency resolution
  • Efficient instruction caching
  • Build cache import/export
  • Nested build job invocations
  • Distributable workers
  • Multiple output formats
  • Pluggable architecture
  • Execution without root privileges

Read the proposal from https://github.com/moby/moby/issues/32925

Introductory blog post https://blog.mobyproject.org/introducing-buildkit-17e056cc5317

Join #buildkit channel on Docker Community Slack

[!NOTE] If you are visiting this repo for the usage of BuildKit-only Dockerfile features like RUN --mount=type=(bind|cache|tmpfs|secret|ssh), please refer to the Dockerfile reference.

[!NOTE] docker build uses Buildx and BuildKit by default since Docker Engine 23.0. You don't need to read this document unless you want to use the full-featured standalone version of BuildKit.

<!-- START doctoc generated TOC please keep comment here to allow auto update --> <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE --> <!-- END doctoc generated TOC please keep comment here to allow auto update -->

Used by

BuildKit is used by the following projects:

Quick start

:information_source: For Kubernetes deployments, see examples/kubernetes.

BuildKit is composed of the buildkitd daemon and the buildctl client. While the buildctl client is available for Linux, macOS, and Windows, the buildkitd daemon is only available for Linux and *Windows currently.

The latest binaries of BuildKit are available here for Linux, macOS, and Windows.

Linux Setup

The buildkitd daemon requires the following components to be installed:

Starting the buildkitd daemon: You need to run buildkitd as the root user on the host.

$ sudo buildkitd

To run buildkitd as a non-root user, see docs/rootless.md.

The buildkitd daemon supports two worker backends: OCI (runc) and containerd.

By default, the OCI (runc) worker is used. You can set --oci-worker=false --containerd-worker=true to use the containerd worker.

We are open to adding more backends.

To start the buildkitd daemon using systemd socket activation, you can install the buildkit systemd unit files. See Systemd socket activation

The buildkitd daemon listens gRPC API on /run/buildkit/buildkitd.sock by default, but you can also use TCP sockets. See Expose BuildKit as a TCP service.

Windows Setup

See instructions and notes at docs/windows.md.

macOS Setup

Homebrew formula (unofficial) is available for macOS.

$ brew install buildkit

The Homebrew formula does not contain the daemon (buildkitd).

For example, Lima can be used for launching the daemon inside a Linux VM.

brew install lima limactl start template://buildkit export BUILDKIT_HOST="unix://$HOME/.lima/buildkit/sock/buildkitd.sock"

Build from source

To build BuildKit from source, see .github/CONTRIBUTING.md.

For a buildctl reference, see this document.

Exploring LLB

BuildKit builds are based on a binary intermediate format called LLB that is used for defining the dependency graph for processes running part of your build. tl;dr: LLB is to Dockerfile what LLVM IR is to C.

  • Marshaled as Protobuf messages
  • Concurrently executable
  • Efficiently cacheable
  • Vendor-neutral (i.e. non-Dockerfile languages can be easily implemented)

See solver/pb/ops.proto for the format definition, and see ./examples/README.md for example LLB applications.

Currently, the following high-level languages have been implemented for LLB:

Exploring Dockerfiles

Frontends are components that run inside BuildKit and convert any build definition to LLB. There is a special frontend called gateway (gateway.v0) that allows using any image as a frontend.

During development, Dockerfile frontend (dockerfile.v0) is also part of the BuildKit repo. In the future, this will be moved out, and Dockerfiles can be built using an external image.

Building a Dockerfile with buildctl

buildctl build \ --frontend=dockerfile.v0 \ --local context=. \ --local dockerfile=. # or buildctl build \ --frontend=dockerfile.v0 \ --local context=. \ --local dockerfile=. \ --opt target=foo \ --opt build-arg:foo=bar

--local exposes local source files from client to the builder. context and dockerfile are the names Dockerfile frontend looks for build context and Dockerfile location.

If the Dockerfile has a different filename it can be specified with --opt filename=./Dockerfile-alternative.

Building a Dockerfile using external frontend

External versions of the Dockerfile frontend are pushed to https://hub.docker.com/r/docker/dockerfile-upstream and https://hub.docker.com/r/docker/dockerfile and can be used with the gateway frontend. The source for the external frontend is currently located in ./frontend/dockerfile/cmd/dockerfile-frontend but will move out of this repository in the future (#163). For automatic build from master branch of this repository docker/dockerfile-upstream:master or docker/dockerfile-upstream:master-labs image can be used.

buildctl build \ --frontend gateway.v0 \ --opt source=docker/dockerfile \ --local context=. \ --local dockerfile=. buildctl build \ --frontend gateway.v0 \ --opt source=docker/dockerfile \ --opt context=https://github.com/moby/moby.git \ --opt build-arg:APT_MIRROR=cdn-fastly.deb.debian.org

Output

By default, the build result and intermediate cache will only remain internally in BuildKit. An output needs to be specified to retrieve the result.

Image/Registry

buildctl build ... --output type=image,name=docker.io/username/image,push=true

To export the image to multiple registries:

buildctl build ... --output type=image,\"name=docker.io/username/image,docker.io/username2/image2\",push=true

To export the cache embed with the image and pushing them to registry together, type registry is required to import the cache, you should specify --export-cache type=inline and --import-cache type=registry,ref=.... To export the cache to a local directly, you should specify --export-cache type=local. Details in Export cache.

buildctl build ...\ --output type=image,name=docker.io/username/image,push=true \ --export-cache type=inline \ --import-cache type=registry,ref=docker.io/username/image

Keys supported by image output:

  • name=<value>: specify image name(s)
  • push=true: push after creating the image
  • push-by-digest=true: push unnamed image
  • registry.insecure=true: push to insecure HTTP registry
  • oci-mediatypes=true: use OCI mediatypes in configuration JSON instead of Docker's
  • unpack=true: unpack image after creation (for use with containerd)
  • dangling-name-prefix=<value>: name image with prefix@<digest>, used for anonymous images
  • name-canonical=true: add additional canonical name name@<digest>
  • compression=<uncompressed|gzip|estargz|zstd>: choose compression type for layers newly created and cached, gzip is default value. estargz should be used with oci-mediatypes=true.
  • compression-level=<value>: compression level for gzip, estargz (0-9) and zstd (0-22)
  • rewrite-timestamp=true: rewrite the file timestamps to the SOURCE_DATE_EPOCH value. See docs/build-repro.md for how to specify the SOURCE_DATE_EPOCH value.
  • force-compression=true: forcefully apply compression option to all layers (including already existing layers)
  • store=true: store the result images to the worker's (e.g. containerd) image store as well as ensures that the image has all blobs in the content store (default true). Ignored if the worker doesn't have image store (e.g. OCI worker).
  • annotation.<key>=<value>: attach an annotation with the respective key and value to the built image
    • Using the extended syntaxes, annotation-<type>.<key>=<value>, annotation[<platform>].<key>=<value> and both combined with annotation-<type>[<platform>].<key>=<value>, allows configuring exactly where to attach the annotation.
    • <type> specifies what object to attach to, and can be any of manifest (the default), manifest-descriptor, index and index-descriptor
    • <platform> specifies which objects to attach to (by default, all), and is the same key passed into the platform opt, see docs/multi-platform.md.
    • See docs/annotations.md for more details.

If credentials are required, buildctl will attempt to read Docker configuration file $DOCKER_CONFIG/config.json. $DOCKER_CONFIG defaults to ~/.docker.

Local directory

The local client will copy the files directly to the client. This is useful if BuildKit is being used for building something else than container images.

buildctl build ... --output type=local,dest=path/to/output-dir

To export specific files use multi-stage builds with a scratch stage and copy the needed files into that stage with `COPY

编辑推荐精选

潮际好麦

潮际好麦

AI赋能电商视觉革命,一站式智能商拍平台

潮际好麦深耕服装行业,是国内AI试衣效果最好的软件。使用先进AIGC能力为电商卖家批量提供优质的、低成本的商拍图。合作品牌有Shein、Lazada、安踏、百丽等65个国内外头部品牌,以及国内10万+淘宝、天猫、京东等主流平台的品牌商家,为卖家节省将近85%的出图成本,提升约3倍出图效率,让品牌能够快速上架。

iTerms

iTerms

企业专属的AI法律顾问

iTerms是法大大集团旗下法律子品牌,基于最先进的大语言模型(LLM)、专业的法律知识库和强大的智能体架构,帮助企业扫清合规障碍,筑牢风控防线,成为您企业专属的AI法律顾问。

SimilarWeb流量提升

SimilarWeb流量提升

稳定高效的流量提升解决方案,助力品牌曝光

稳定高效的流量提升解决方案,助力品牌曝光

Sora2视频免费生成

Sora2视频免费生成

最新版Sora2模型免费使用,一键生成无水印视频

最新版Sora2模型免费使用,一键生成无水印视频

Transly

Transly

实时语音翻译/同声传译工具

Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手,它拥有超精准的音频识别翻译能力,几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球,无论你是留学生、商务人士、韩剧美剧爱好者,还是出国游玩、多国会议、跨国追星等等,都可以满足你所有需要同传的场景需求,线上线下通用,扫除语言障碍,让全世界的语言交流不再有国界。

讯飞绘文

讯飞绘文

选题、配图、成文,一站式创作,让内容运营更高效

讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。

AI助手热门AI工具AI创作AI辅助写作讯飞绘文内容运营个性化文章多平台分发
TRAE编程

TRAE编程

AI辅助编程,代码自动修复

Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。

热门AI工具生产力协作转型TraeAI IDE
商汤小浣熊

商汤小浣熊

最强AI数据分析助手

小浣熊家族Raccoon,您的AI智能助手,致力于通过先进的人工智能技术,为用户提供高效、便捷的智能服务。无论是日常咨询还是专业问题解答,小浣熊都能以快速、准确的响应满足您的需求,让您的生活更加智能便捷。

imini AI

imini AI

像人一样思考的AI智能体

imini 是一款超级AI智能体,能根据人类指令,自主思考、自主完成、并且交付结果的AI智能体。

Keevx

Keevx

AI数字人视频创作平台

Keevx 一款开箱即用的AI数字人视频创作平台,广泛适用于电商广告、企业培训与社媒宣传,让全球企业与个人创作者无需拍摄剪辑,就能快速生成多语言、高质量的专业视频。

下拉加载更多