| Travis CI | Bazel CI |
|---|---|
 |
 |
- container_image (example)
- container_bundle (example)
- container_import
- container_load
- container_pull (example)
- container_push (example)
These rules used to be docker_build, docker_push, etc. and the aliases for
these (mostly) legacy names still exist largely for backwards-compatibility. We
also have early-stage oci_image, oci_push, etc. aliases for folks that
enjoy the consistency of a consistent rule prefix. The only place the
format-specific names currently do any more than alias things is in foo_push,
where they also specify the appropriate format as which to publish the image.
This repository contains a set of rules for pulling down base images, augmenting them with build artifacts and assets, and publishing those images. These rules do not require / use Docker for pulling, building, or pushing images. This means:
- They can be used to develop Docker containers on Windows / OSX without
boot2dockerordocker-machineinstalled. - They do not require root access on your workstation.
Also, unlike traditional container builds (e.g. Dockerfile), the Docker images
produced by container_image are deterministic / reproducible.
To get started with building Docker images, check out the examples that build the same images using both rules_docker and a Dockerfile.
NOTE: container_push and container_pull make use of
google/containerregistry for
registry interactions.
- py_image (signature)
- py3_image (signature)
- nodejs_image (usage)
- java_image (signature)
- war_image (signature)
- scala_image (signature)
- groovy_image (signature)
- cc_image (signature)
- go_image (signature)
- rust_image (signature)
- d_image (signature)
It is notable that: cc_image, go_image, rust_image, and d_image
also allow you to specify an external binary target.
In addition to low-level rules for building containers, this repository
provides a set of higher-level rules for containerizing applications. The idea
behind these rules is to make containerizing an application built via a
lang_binary rule as simple as changing it to lang_image.
By default these higher level rules make use of the distroless language runtimes, but these
can be overridden via the base="..." attribute (e.g. with a container_pull
or container_image target).
Note also that these rules do not expose any docker related attributes. If you
need to add a custom env or symlink to a lang_image, you must use
container_image targets for this purpose. Specifically, you can use as base for your
lang_image target a container_image target that adds e.g., custom env or symlink.
Please see go_image (custom base) for an example.
Add the following to your WORKSPACE file to add the external repositories:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# Download the rules_docker repository at release v0.8.0
http_archive(
name = "io_bazel_rules_docker",
sha256 = "3556d4972571f288f8c43378295d84ed64fef5b1a875211ee1046f9f6b4258fa",
strip_prefix = "rules_docker-0.8.0",
urls = ["https://github.com/bazelbuild/rules_docker/archive/v0.8.0.tar.gz"],
)
# OPTIONAL: Call this to override the default docker toolchain configuration.
# This call should be placed BEFORE the call to "container_repositories" below
# to actually override the default toolchain configuration.
# Note this is only required if you actually want to call
# docker_toolchain_configure with a custom attr; please read the toolchains
# docs in /toolchains/docker/ before blindly adding this to your WORKSPACE.
load("@io_bazel_rules_docker//toolchains/docker:toolchain.bzl",
docker_toolchain_configure="toolchain_configure"
)
docker_toolchain_configure(
name = "docker_config",
# OPTIONAL: Path to a directory which has a custom docker client config.json.
# See https://docs.docker.com/engine/reference/commandline/cli/#configuration-files
# for more details.
client_config="/path/to/docker/client/config",
)
# This is NOT needed when going through the language lang_image
# "repositories" function(s).
load(
"@io_bazel_rules_docker//repositories:repositories.bzl",
container_repositories = "repositories",
)
container_repositories()
load(
"@io_bazel_rules_docker//container:container.bzl",
"container_pull",
)
container_pull(
name = "java_base",
registry = "gcr.io",
repository = "distroless/java",
# 'tag' is also supported, but digest is encouraged for reproducibility.
digest = "sha256:deadbeef",
)Note: Bazel does not deal well with diamond dependencies. If the repositories that
are imported by container_repositories() have already been imported (at a
different version) by other rules you called in your WORKSPACE, which are
placed above the call to container_repositories(), arbitrary errors might
ocurr. If you get errors related to external repositories, you will likely
not be able to use container_repositories() and will have to import
directly in your WORKSPACE all the required dependencies (see the most up
to date impl of container_repositories() for details).
Suppose you have a container_image target //my/image:helloworld:
container_image(
name = "helloworld",
...
)You can load this into your local Docker client by running:
bazel run my/image:helloworld.
For the lang_image targets, this will also run the
container to maximize compatibility with lang_binary rules. You can suppress
this behavior by passing the single flag: bazel run :foo -- --norun
Alternatively, you can build a docker load compatible bundle with:
bazel build my/image:helloworld.tar. This will produce the file:
bazel-bin/my/image/helloworld.tar, which you can load into
your local Docker client by running:
docker load -i bazel-bin/my/image/helloworld.tar. Building
this target can be expensive for large images.
These work with both container_image, container_bundle, and the
lang_image rules. For everything except
container_bundle, the image name will be bazel/my/image:helloworld.
For container_bundle, it will apply the tags you have specified.
You can use these rules to access private images using standard Docker authentication methods. e.g. to utilize the Google Container Registry. See here for authentication methods.
See also:
Once you've setup your docker client configuration, see here for an example of how to use container_pull with custom docker authentication credentials and here for an example of how to use container_push with custom docker authentication credentials.
A common request from folks using container_push or container_bundle is to
be able to vary the tag that is pushed or embedded. There are two options
at present for doing this.
The first option is to use stamping. Stamping is enabled when a supported
attribute contains a python format placeholder (eg {BUILD_USER}).
# A common pattern when users want to avoid trampling
# on each other's images during development.
container_push(
name = "publish",
format = "Docker",
# Any of these components may have variables.
registry = "gcr.io",
repository = "my-project/my-image",
tag = "{BUILD_USER}",
)The next natural question is: "Well what variables can I use?" This
option consumes the workspace-status variables Bazel defines in
stable-status.txt and volatile-status.txt. These files will appear
in the target's runfiles:
$ bazel build //docker/testdata:push_stamp
...
$ cat bazel-bin/docker/testdata/push_stamp.runfiles/io_bazel_rules_docker/stable-status.txt
BUILD_EMBED_LABEL
BUILD_HOST bazel
BUILD_USER mattmoor
$ cat bazel-bin/docker/testdata/push_stamp.runfiles/io_bazel_rules_docker/volatile-status.txt
BUILD_TIMESTAMP 1498740967769
You can augment these variables via --workspace_status_command,
including through the use of .bazelrc.
The second option is to employ Makefile-style variables:
container_bundle(
name = "bundle",
images = {
"gcr.io/$(project)/frontend:latest": "//frontend:image",
"gcr.io/$(project)/backend:latest": "//backend:image",
}
)These variables are specified on the CLI using:
bazel build --define project=blah //path/to:bundleBy default the lang_image rules use the distroless base runtime images,
which are optimized to be the minimal set of things your application needs
at runtime. That can make debugging these containers difficult because they
lack even a basic shell for exploring the filesystem.
To address this, we publish variants of the distroless runtime images tagged
:debug, which are the exact-same images, but with additions such as busybox
to make debugging easier.
For example (in this repo):
$ bazel run -c dbg testdata:go_image
...
INFO: Build completed successfully, 5 total actions
INFO: Running command line: bazel-bin/testdata/go_image
Loaded image ID: sha256:9c5c2167a1db080a64b5b401b43b3c5cdabb265b26cf7a60aabe04a20da79e24
Tagging 9c5c2167a1db080a64b5b401b43b3c5cdabb265b26cf7a60aabe04a20da79e24 as bazel/testdata:go_image
Hello, world!
$ docker run -ti --rm --entrypoint=sh bazel/testdata:go_image -c "echo Hello, busybox."
Hello, busybox.container_image(
name = "app",
# References container_pull from WORKSPACE (above)
base = "@java_base//image",
files = ["//java/com/example/app:Hello_deploy.jar"],
cmd = ["Hello_deploy.jar"]
)Hint: if you want to put files in specific directories inside the image
use pkg_tar rule
to create the desired directory structure and pass that to container_image via
tars attribute. Note you might need to set strip_prefix = "." or strip_prefix = "{some directory}"
in your rule for the files to not be flattened.
See Bazel upstream issue 2176 and
rules_docker issue 317
for more details.
To use cc_image, add the following to WORKSPACE:
load(
"@io_bazel_rules_docker//cc:image.bzl",
_cc_image_repos = "repositories",
)
_cc_image_repos()Then in your BUILD file, simply rewrite cc_binary to cc_image with the
following import:
load("@io_bazel_rules_docker//cc:image.bzl", "cc_image")
cc_image(
name = "cc_image",
srcs = ["cc_image.cc"],
deps = [":cc_image_library"],
)To use cc_image (or go_image, d_image, rust_image) with an external
cc_binary (or the like) target, then your BUILD file should instead look
like:
load("@io_bazel_rules_docker//cc:image.bzl", "cc_image")
cc_binary(
name = "cc_binary",
srcs = ["cc_binary.cc"],
deps = [":cc_library"],
)
cc_image(
name = "cc_image",
binary = ":cc_binary",
)If you need to modify somehow the container produced by
cc_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example below.
To use py_image, add the following to WORKSPACE:
load(
"@io_bazel_rules_docker//python:image.bzl",
_py_image_repos = "repositories",
)
_py_image_repos()Then in your BUILD file, simply rewrite py_binary to py_image with the
following import:
load("@io_bazel_rules_docker//python:image.bzl", "py_image")
py_image(
name = "py_image",
srcs = ["py_image.py"],
deps = [":py_image_library"],
main = "py_image.py",
)If you need to modify somehow the container produced by
py_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example below.
If you are using py_image with a custom base that has python tools installed
in a location different to the default base, please see
Python tools.
For Python and Java's lang_image rules, you can factor
dependencies that don't change into their own layers by overriding the
layers=[] attribute. Consider this sample from the rules_k8s repository:
py_image(
name = "server",
srcs = ["server.py"],
# "layers" is just like "deps", but it also moves the dependencies each into
# their own layer, which can dramatically improve developer cycle time. For
# example here, the grpcio layer is ~40MB, but the rest of the app is only
# ~400KB. By partitioning things this way, the large grpcio layer remains
# unchanging and we can reduce the amount of image data we repush by ~99%!
layers = [
requirement("grpcio"),
"//examples/hellogrpc/proto:py",
],
main = "server.py",
)You can also implement more complex fine layering strategies by using the
py_layer rule and its filter attribute. For example:
# Suppose that we are synthesizing an image that depends on a complex set
# of libraries that we want to break into layers.
LIBS = [
"//pkg/complex_library",
# ...
]
# First, we extract all transitive dependencies of LIBS that are under //pkg/common.
py_layer(
name = "common_deps",
deps = LIBS,
filter = "//pkg/common",
)
# Then, we further extract all external dependencies of the deps under //pkg/common.
py_layer(
name = "common_external_deps",
deps = [":common_deps"],
filter = "@",
)
# We also extract all external dependencies of LIBS, which is a superset of
# ":common_external_deps".
py_layer(
name = "external_deps",
deps = LIBS,
filter = "@",
)
# Finally, we create the image, stacking the above filtered layers on top of one
# another in the "layers" attribute. The layers are applied in order, and any
# dependencies already added to the image will not be added again. Therefore,
# ":external_deps" will only add the external dependencies not present in
# ":common_external_deps".
py_image(
name = "image",
deps = LIBS,
layers = [
":common_external_deps",
":common_deps",
":external_deps",
],
# ...
)To use a Python 3 runtime instead of the default of Python 2, use py3_image,
instead of py_image. The other semantics are identical.
If you need to modify somehow the container produced by
py3_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example below.
If you are using py3_image with a custom base that has python tools installed
in a location different to the default base, please see
Python tools.
It is notable that unlike the other image rules, nodejs_image is not
currently using the gcr.io/distroless/nodejs image for a handful of reasons.
This is a switch we plan to make, when we can manage it. We are currently
utilizing the gcr.io/google-appengine/debian9 image as our base.
To use nodejs_image, add the following to WORKSPACE:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
http_archive(
name = "build_bazel_rules_nodejs",
# Replace with a real SHA256 checksum
sha256 = "{SHA256}"
# Replace with a real commit SHA
strip_prefix = "rules_nodejs-{HEAD}",
urls = ["https://github.com/bazelbuild/rules_nodejs/archive/{HEAD}.tar.gz"],
)
load("@build_bazel_rules_nodejs//:defs.bzl", "node_repositories", "npm_install")
# Download Node toolchain, etc.
node_repositories(package_json = ["//:package.json"])
# Install your declared Node.js dependencies
npm_install(
name = "npm_deps",
package_json = "//:package.json",
)
# Download base images, etc.
load(
"@io_bazel_rules_docker//nodejs:image.bzl",
_nodejs_image_repos = "repositories",
)
_nodejs_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite nodejs_binary to nodejs_image with
the following import:
load("@io_bazel_rules_docker//nodejs:image.bzl", "nodejs_image")
nodejs_image(
name = "nodejs_image",
entry_point = "your_workspace/path/to/file.js",
# This will be put into its own layer.
node_modules = "@npm_deps//:node_modules",
data = [":file.js"],
...
)If you need to modify somehow the container produced by
nodejs_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example below.
To use go_image, add the following to WORKSPACE:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# You *must* import the Go rules before setting up the go_image rules.
http_archive(
name = "io_bazel_rules_go",
# Replace with a real SHA256 checksum
sha256 = "{SHA256}"
# Replace with a real commit SHA
strip_prefix = "rules_go-{HEAD}",
urls = ["https://github.com/bazelbuild/rules_go/archive/{HEAD}.tar.gz"],
)
load(
"@io_bazel_rules_docker//go:image.bzl",
_go_image_repos = "repositories",
)
_go_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite go_binary to go_image with the
following import:
load("@io_bazel_rules_docker//go:image.bzl", "go_image")
go_image(
name = "go_image",
srcs = ["main.go"],
importpath = "github.com/your/path/here",
)Notice that it is important to explicitly build this target with the
--platforms=@io_bazel_rules_go//go/toolchain:linux_amd64 flag
as the binary should be built for Linux since it will run in a Linux container.
If you need to modify somehow the container produced by
go_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this and
see example below.
To use a custom base image, with any of the lang_image
rules, you can override the default base="..." attribute. Consider this
modified sample from the distroless repository:
load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar")
# Create a passwd file with a root and nonroot user and uid.
passwd_entry(
username = "root",
uid = 0,
gid = 0,
name = "root_user",
)
passwd_entry(
username = "nonroot",
info = "nonroot",
uid = 1002,
name = "nonroot_user",
)
passwd_file(
name = "passwd",
entries = [
":root_user",
":nonroot_user",
],
)
# Create a tar file containing the created passwd file
pkg_tar(
name = "passwd_tar",
srcs = [":passwd"],
mode = "0o644",
package_dir = "etc",
)
# Include it in our base image as a tar.
container_image(
name = "passwd_image",
base = "@go_image_base//image",
tars = [":passwd_tar"],
user = "nonroot",
)
# Simple go program to print out the username and uid.
go_image(
name = "user",
srcs = ["user.go"],
# Override the base image.
base = ":passwd_image",
)To use java_image, add the following to WORKSPACE:
load(
"@io_bazel_rules_docker//java:image.bzl",
_java_image_repos = "repositories",
)
_java_image_repos()Then in your BUILD file, simply rewrite java_binary to java_image with the
following import:
load("@io_bazel_rules_docker//java:image.bzl", "java_image")
java_image(
name = "java_image",
srcs = ["Binary.java"],
# Put these runfiles into their own layer.
layers = [":java_image_library"],
main_class = "examples.images.Binary",
)If you need to modify somehow the container produced by
java_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example.
To use war_image, add the following to WORKSPACE:
load(
"@io_bazel_rules_docker//java:image.bzl",
_java_image_repos = "repositories",
)
_java_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite java_war to war_image with the
following import:
load("@io_bazel_rules_docker//java:image.bzl", "war_image")
war_image(
name = "war_image",
srcs = ["Servlet.java"],
# Put these JARs into their own layers.
layers = [
":java_image_library",
"@javax_servlet_api//jar:jar",
],
)The produced image uses Jetty 9.x to serve the web application. Servlets included in the web application need to follow the API specification 3.0. For best compatibility, use a Servlet dependency provided by the Jetty project.
A Servlet implementation needs to declare the @WebServlet annotation to be auto-discovered. The use of a web.xml to declare the Servlet URL mapping is not supported.
If you need to modify somehow the container produced by
war_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example.
To use scala_image, add the following to WORKSPACE:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# You *must* import the Scala rules before setting up the scala_image rules.
http_archive(
name = "io_bazel_rules_scala",
# Replace with a real SHA256 checksum
sha256 = "{SHA256}"
# Replace with a real commit SHA
strip_prefix = "rules_scala-{HEAD}",
urls = ["https://github.com/bazelbuild/rules_scala/archive/{HEAD}.tar.gz"],
)
load("@io_bazel_rules_scala//scala:scala.bzl", "scala_repositories")
scala_repositories()
load(
"@io_bazel_rules_docker//scala:image.bzl",
_scala_image_repos = "repositories",
)
_scala_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite scala_binary to scala_image with the
following import:
load("@io_bazel_rules_docker//scala:image.bzl", "scala_image")
scala_image(
name = "scala_image",
srcs = ["Binary.scala"],
main_class = "examples.images.Binary",
)If you need to modify somehow the container produced by
scala_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example.
To use groovy_image, add the following to WORKSPACE:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# You *must* import the Groovy rules before setting up the groovy_image rules.
http_archive(
name = "io_bazel_rules_groovy",
# Replace with a real SHA256 checksum
sha256 = "{SHA256}"
# Replace with a real commit SHA
strip_prefix = "rules_groovy-{HEAD}",
urls = ["https://github.com/bazelbuild/rules_groovy/archive/{HEAD}.tar.gz"],
)
load("@io_bazel_rules_groovy//groovy:groovy.bzl", "groovy_repositories")
groovy_repositories()
load(
"@io_bazel_rules_docker//groovy:image.bzl",
_groovy_image_repos = "repositories",
)
_groovy_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite groovy_binary to groovy_image with the
following import:
load("@io_bazel_rules_docker//groovy:image.bzl", "groovy_image")
groovy_image(
name = "groovy_image",
srcs = ["Binary.groovy"],
main_class = "examples.images.Binary",
)If you need to modify somehow the container produced by
groovy_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example.
To use rust_image, add the following to WORKSPACE:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# You *must* import the Rust rules before setting up the rust_image rules.
http_archive(
name = "io_bazel_rules_rust",
# Replace with a real SHA256 checksum
sha256 = "{SHA256}"
# Replace with a real commit SHA
strip_prefix = "rules_rust-{HEAD}",
urls = ["https://github.com/bazelbuild/rules_rust/archive/{HEAD}.tar.gz"],
)
load("@io_bazel_rules_rust//rust:repositories.bzl", "rust_repositories")
rust_repositories()
load(
"@io_bazel_rules_docker//rust:image.bzl",
_rust_image_repos = "repositories",
)
_rust_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite rust_binary to rust_image with the
following import:
load("@io_bazel_rules_docker//rust:image.bzl", "rust_image")
rust_image(
name = "rust_image",
srcs = ["main.rs"],
)If you need to modify somehow the container produced by
rust_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example.
To use d_image, add the following to WORKSPACE:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# You *must* import the D rules before setting up the d_image rules.
http_archive(
name = "io_bazel_rules_d",
# Replace with a real SHA256 checksum
sha256 = "{SHA256}"
# Replace with a real commit SHA
strip_prefix = "rules_d-{HEAD}",
urls = ["https://github.com/bazelbuild/rules_d/archive/{HEAD}.tar.gz"],
)
load("@io_bazel_rules_d//d:d.bzl", "d_repositories")
d_repositories()
load(
"@io_bazel_rules_docker//d:image.bzl",
_d_image_repos = "repositories",
)
_d_image_repos()Note: See note about diamond dependencies in setup
if you run into issues related to external repos after adding these
lines to your WORKSPACE.
Then in your BUILD file, simply rewrite d_binary to d_image with the
following import:
load("@io_bazel_rules_docker//d:image.bzl", "d_image")
d_image(
name = "d_image",
srcs = ["main.d"],
)If you need to modify somehow the container produced by
d_image (e.g., env, symlink), see note above in
Language Rules Overview about how to do this
and see go_image (custom base) example.
NOTE: all application image rules support the
argsstring_list attribute. If specified, they will be appended directly after the container ENTRYPOINT binary name.
container_bundle(
name = "bundle",
images = {
# A set of images to bundle up into a single tarball.
"gcr.io/foo/bar:bazz": ":app",
"gcr.io/foo/bar:blah": "//my:sidecar",
"gcr.io/foo/bar:booo": "@your//random:image",
}
)In WORKSPACE:
container_pull(
name = "base",
registry = "gcr.io",
repository = "my-project/my-base",
# 'tag' is also supported, but digest is encouraged for reproducibility.
digest = "sha256:deadbeef",
)This can then be referenced in BUILD files as @base//image.
To get the correct digest one can run docker manifest inspect gcr.io/my-project/my-base:tag once experimental docker cli featuers are enabled.
See here for an example of how to use container_pull with custom docker authentication credentials.
This target pushes on bazel run :push_foo:
container_push(
name = "push_foo",
image = ":foo",
format = "Docker",
registry = "gcr.io",
repository = "my-project/my-image",
tag = "dev",
)We also support the docker_push (from docker/docker.bzl) and oci_push
(from oci/oci.bzl) aliases, which bake in the format = "..." attribute.
See here for an example of how to use container_push with custom docker authentication credentials.
If you wish to use container_push using custom docker authentication credentials,
in WORKSPACE:
# Download the rules_docker repository
http_archive(
name = "io_bazel_rules_docker",
...
)
# Load the macro that allows you to customize the docker toolchain configuration.
load("@io_bazel_rules_docker//toolchains/docker:toolchain.bzl",
docker_toolchain_configure="toolchain_configure"
)
docker_toolchain_configure(
name = "docker_config",
# Replace this with a path to a directory which has a custom docker client
# config.json. Docker allows you to specify custom authentication credentials
# in the client configuration JSON file.
# See https://docs.docker.com/engine/reference/commandline/cli/#configuration-files
# for more details.
client_config="/path/to/docker/client/config",
)In BUILD file:
load("@io_bazel_rules_docker//container:container.bzl", "container_push")
container_push(
name = "push_foo",
image = ":foo",
format = "Docker",
registry = "gcr.io",
repository = "my-project/my-image",
tag = "dev",
)In WORKSPACE:
container_pull(
name = "official_ubuntu",
registry = "index.docker.io",
repository = "library/ubuntu",
tag = "14.04",
)This can then be referenced in BUILD files as @official_ubuntu//image.
In WORKSPACE:
container_pull(
name = "etcd",
registry = "quay.io",
repository = "coreos/etcd",
tag = "latest",
)This can then be referenced in BUILD files as @etcd//image.
In WORKSPACE:
container_pull(
name = "artifactory",
registry = "docker.bintray.io",
repository = "jfrog/artifactory-pro",
)This can then be referenced in BUILD files as @artifactory//image.
In WORKSPACE:
container_pull(
name = "gitlab",
registry = "registry.gitlab.com",
repository = "username/project/image",
tag = "tag",
)This can then be referenced in BUILD files as @gitlab//image.
If you specified a docker client directory using the "client_config" attribute to the docker toolchain configuration described here, you can use a container_pull that uses the authentication credentials from the specified docker client directory as follows:
In WORKSPACE:
load("@io_bazel_rules_docker//toolchains/docker:toolchain.bzl",
docker_toolchain_configure="toolchain_configure"
)
# Configure the docker toolchain.
docker_toolchain_configure(
name = "docker_config",
# Path to the directory which has a custom docker client config.json with
# authentication credentials for registry.gitlab.com (used in this example).
client_config="/path/to/docker/client/config",
)
# Load the custom version of container_pull created by the docker toolchain
# configuration.
load("@docker_config//:pull.bzl", authenticated_container_pull="container_pull")
authenticated_container_pull(
name = "gitlab",
registry = "registry.gitlab.com",
repository = "username/project/image",
tag = "tag",
)This can then be referenced in BUILD files as @gitlab//image.
NOTE: This will only work on systems with Python >2.7.6
Starting with Bazel 0.25.0 it's possible to configure python toolchains
for rules_docker.
To use these features you need to enable the flags in the .bazelrc
file at the root of this project.
Use of these features require a python toolchain to be registered.
//py_images/image.bzl:deps and //py3_images/image.bzl:deps register a
default python toolchain (//toolchains/python:container_py_toolchain)
that defines the path to python tools inside the default container used
for these rules.
If you are using a custom base for py_image or py3_image builds that has
python tools installed in a different location to those defined in
//toolchains/python:container_py_toolchain, you will need to create a
toolchain that points to these paths and register it before the call to
py*_images/image.bzl:deps in your WORKSPACE.
Until Bazel 0.26.0 is relesed, registration of the default python toolchain will result in all python targets using that same toolchain, which might result in errors if any of those targets need to run locally. Once Bazel 0.26.0 is out, this default toolchain will only be compatible with python targets that run inside a container and will not interfere with other python targets.
Use of python toolchain features, currently, only supports picking one
version of python for execution of host tools. rules_docker heavily depends
on execution of python host tools that are only compatible with python 2.
Flags in the recommended .bazelrc file force all host tools to use python 2.
If your project requires using host tools that are only compatible with
python 3 you will not be able to use these features at the moment. We
expect this issue to be resolved before use of python toolchain features
becomes the default.
The digest references to the distroless base images must be updated over time
to pick up bug fixes and security patches. To facilitate this, the files
containing the digest references are generated by tools/update_deps.py. To
update all of the dependencies, please run (from the root of the repository):
./update_deps.shImage references should not be updated individually because these images have shared layers and letting them diverge could result in sub-optimal push and pull performance.
container_pull(name, registry, repository, digest, tag)A repository rule that pulls down a Docker base image in a manner suitable for
use with container_image's base attribute.
NOTE: container_pull now supports authentication using custom docker client configuration. See here for details.
NOTE: Set PULLER_TIMEOUT env variable to change the default 600s timeout.
NOTE: Set DOCKER_REPO_CACHE env variable to make the container puller
cache downloaded layers at the directory specified as a value to this env
variable. The caching feature hasn't been thoroughly tested and may be thread
unsafe. If you notice flakiness after enabling it, see the warning below on how
to workaround it.
NOTE: container_pull is suspected to have thread safety issues. To
ensure multiple container_pull(s) don't execute concurrently, please use the
bazel startup flag --loading_phase_threads=1 in your bazel invocation.
| Attributes | |
|---|---|
name |
Unique name for this repository rule. |
registry |
The registry from which to pull the base image. |
repository |
The `repository` of images to pull from. |
digest |
The `digest` of the Docker image to pull from the specified `repository`. Note: For reproducible builds, use of `digest` is recommended. |
tag |
The `tag` of the Docker image to pull from the specified `repository`. If neither this nor `digest` is specified, this attribute defaults to `latest`. If both are specified, then `tag` is ignored. Note: For reproducible builds, use of `digest` is recommended. |
os |
When the specified image refers to a multi-platform
manifest list, the desired operating system. For example,
|
os_version |
When the specified image refers to a multi-platform
manifest list, the desired operating system version. For example,
|
os_features |
When the specified image refers to a multi-platform
manifest list, the desired operating system features. For example,
on Windows this might be |
architecture |
When the specified image refers to a multi-platform
manifest list, the desired CPU architecture. For example,
|
cpu_variant |
When the specified image refers to a multi-platform
manifest list, the desired CPU variant. For example, for ARM you
may need to use |
platform_features |
When the specified image refers to a multi-platform
manifest list, the desired features. For example, this may
include CPU features such as |
docker_client_config |
Specifies the directory to look for the docker client configuration. Don't use this directly.
Specify the docker configuration directory using a custom docker toolchain configuration. Look
for the When left unspecified (ie not set explicitly or set by the docker toolchain), docker will use the directory specified via the DOCKER_CONFIG environment variable. If DOCKER_CONFIG isn't set, docker falls back to $HOME/.docker. |
container_push(name, image, registry, repository, tag)An executable rule that pushes a Docker image to a Docker registry on bazel run.
NOTE: container_push now supports authentication using custom docker client configuration. See here for details.
| Attributes | |
|---|---|
name |
Unique name for this rule. |
format |
The desired format of the published image. Currently, this supports
|
image |
The label containing a Docker image to publish. |
registry |
The registry to which to publish the image. This field supports stamp variables. |
repository |
The `repository` of images to which to push. This field supports stamp variables. |
tag |
The `tag` of the Docker image to push to the specified `repository`. This attribute defaults to `latest`. This field supports stamp variables. |
stamp |
Deprecated: it is now automatically inferred. If true, enable use of workspace status variables
(e.g. These fields are specified in the tag using using Python format
syntax, e.g.
|
container_layer(data_path, directory, files, mode, tars, debs, symlinks, env)A rule that assembles data into a tarball which can be use as in layers attr in container_image rule.
| Implicit output targets | |
|---|---|
name-layer.tar |
A tarball of current layer
A data tarball corresponding to the layer. |
| Attributes | |
|---|---|
name |
Name, required
A unique name for this rule. |
data_path |
String, optional
Root path of the files. The directory structure from the files is preserved inside the Docker image, but a prefix path determined by `data_path` is removed from the directory structure. This path can be absolute from the workspace root if starting with a `/` or relative to the rule's directory. A relative path may starts with "./" (or be ".") but cannot use go up with "..". By default, the `data_path` attribute is unused, and all files should have no prefix. |
directory |
String, optional
Target directory. The directory in which to expand the specified files, defaulting to '/'. Only makes sense accompanying one of files/tars/debs. |
files |
List of files, optional
File to add to the layer. A list of files that should be included in the Docker image. |
mode |
String, default to 0o555
Set the mode of files added by the |
tars |
List of files, optional
Tar file to extract in the layer. A list of tar files whose content should be in the Docker image. |
debs |
List of files, optional
Debian package to install. A list of debian packages that will be installed in the Docker image. |
symlinks |
Dictionary, optional
Symlinks to create in the Docker image.
|
env |
Dictionary from strings to strings, optional
Dictionary from environment variable names to their values when running the Docker image.
The values of this field support make variables (e.g., |
container_image(name, base, data_path, directory, files, legacy_repository_naming, mode, tars, debs, symlinks, entrypoint, cmd, creation_time, env, labels, ports, volumes, workdir, layers, repository)| Implicit output targets | |
|---|---|
name.tar |
The full Docker image
A full Docker image containing all the layers, identical to
what |
name.digest |
The full Docker image's digest
An image digest that can be used to refer to that image. Unlike tags, digest references are immutable i.e. always refer to the same content. |
name-layer.tar |
An image of the current layer
A Docker image containing only the layer corresponding to that target. It is used for incremental loading of the layer. Note: this target is not suitable for direct consumption. It is used for incremental loading and non-docker rules should depends on the Docker image (name.tar) instead. |
name |
Incremental image loader
The incremental image loader. It will load only changed layers inside the Docker registry. |
| Attributes | |
|---|---|
name |
Name, required
A unique name for this rule. |
base |
File, optional
The base layers on top of which to overlay this layer, equivalent to FROM. |
data_path |
String, optional
Root path of the files. The directory structure from the files is preserved inside the Docker image, but a prefix path determined by `data_path` is removed from the directory structure. This path can be absolute from the workspace root if starting with a `/` or relative to the rule's directory. A relative path may starts with "./" (or be ".") but cannot use go up with "..". By default, the `data_path` attribute is unused, and all files should have no prefix. |
directory |
String, optional
Target directory. The directory in which to expand the specified files, defaulting to '/'. Only makes sense accompanying one of files/tars/debs. |
files |
List of files, optional
File to add to the layer. A list of files that should be included in the Docker image. |
legacy_repository_naming |
Bool, default to False
Whether to use the legacy strategy for setting the repository name
embedded in the resulting tarball.
e.g. |
mode |
String, default to 0o555
Set the mode of files added by the |
tars |
List of files, optional
Tar file to extract in the layer. A list of tar files whose content should be in the Docker image. |
debs |
List of files, optional
Debian packages to extract.
Deprecated: A list of debian packages that will be extracted in the Docker image.
Note that this doesn't actually install the packages. Installation needs apt
or apt-get which need to be executed within a running container which
|
symlinks |
Dictionary, optional
Symlinks to create in the Docker image.
|
user |
String, optional
The user that the image should run as. Because building the image never happens inside a Docker container, this user does not affect the other actions (e.g., adding files). This field supports stamp variables. |
entrypoint |
String or string list, optional
List of entrypoints to add in the image.
Set This field supports stamp variables. |
cmd |
String or string list, optional
List of commands to execute in the image.
Set This field supports stamp variables. |
creation_time |
String, optional, default to {BUILD_TIMESTAMP} when stamp = True, otherwise 0
The image's creation timestamp. Acceptable formats: Integer or floating point seconds since Unix Epoch, RFC 3339 date/time. This field supports stamp variables. |
env |
Dictionary from strings to strings, optional
Dictionary from environment variable names to their values when running the Docker image.
The values of this field support make variables (e.g., |
labels |
Dictionary from strings to strings, optional
The values of this field support stamp variables. |
ports |
String list, optional
|
volumes |
String list, optional
|
workdir |
String, optional
Initial working directory when running the Docker image. Because building the image never happens inside a Docker container, this working directory does not affect the other actions (e.g., adding files). This field supports stamp variables. |
layers |
Label list, optional
List of `container_layer` targets. The data from each `container_layer` will be part of container image, and the environment variable will be available in the image as well. |
repository |
String, default to `bazel`
The repository for the default tag for the image. Images generated by `container_image` are tagged by default to `bazel/package_name:target` for a `container_image` target at `//package/name:target`. Setting this attribute to `gcr.io/dummy` would set the default tag to `gcr.io/dummy/package_name:target`. |
stamp |
If true, enable use of workspace status variables
(e.g. These fields are specified in attributes using using Python format
syntax, e.g. |
launcher |
If present, prefix the image's ENTRYPOINT with this file. Note that the launcher should be a container-compatible (OS & Arch) single executable file without any runtime dependencies (as none of its runfiles will be included in the image). |
launcher_args |
Optional arguments for the |
legacy_run_behavior |
If set to False, Note that it defaults to |
docker_run_flags |
Optional flags to use with Only used when |
| Toolchains | |
|---|---|
@io_bazel_rules_docker//toolchains/docker:toolchain_type |
See How to use the Docker Toolchain for details |
container_bundle(name, images)A rule that aliases and saves N images into a single docker save tarball.
| Attributes | |
|---|---|
name |
Unique name for this rule. |
images |
A collection of the images to save into the tarball. The keys are the tags with which to alias the image specified by the
value. These tags may contain make variables ( The values may be the output of |
stamp |
Deprecated: it is now automatically inferred. If true, enable use of workspace status variables
(e.g. These fields are specified in the tag using using Python format
syntax, e.g.
|
container_import(name, config, layers)A rule that imports a docker image into our intermediate form.
| Attributes | |
|---|---|
name |
Unique name for this rule. |
config |
A json configuration file containing the image's metadata. This appears in `docker save` tarballs as `.json` and is referenced by `manifest.json` in the config field. |
layers |
The list of layer |
container_load(name, file)A repository rule that examines the contents of a docker save tarball and
creates a container_import target. The created target can be referenced as
@label_name//image.
| Attributes | |
|---|---|
name |
Unique name for this rule. |
file |
A label targeting a single file which is a compressed or uncompressed tar, as obtained through `docker save IMAGE`. |
Here's a (non-exhaustive) list of companies that use rules_docker in production. Don't see yours? You can add it in a PR!