Python Rules for Bazel

Overview

This repository is the home of the core Python rules -- py_library, py_binary, py_test, py_proto_library, and related symbols that provide the basis for Python support in Bazel. It also contains package installation rules for integrating with PyPI and other indices.

Documentation for rules_python lives in the docs/ directory and in the Bazel Build Encyclopedia.

Examples live in the examples directory.

Currently, the core rules build into the Bazel binary, and the symbols in this repository are simple aliases. However, we are migrating the rules to Starlark and removing them from the Bazel binary. Therefore, the future-proof way to depend on Python rules is via this repository. SeeMigrating from the Bundled Rules below.

The core rules are stable. Their implementation in Bazel is subject to Bazel's backward compatibility policy. Once migrated to rules_python, they may evolve at a different rate, but this repository will still follow semantic versioning.

The Bazel community maintains this repository. Neither Google nor the Bazel team provides support for the code. However, this repository is part of the test suite used to vet new Bazel releases. See How to contribute page for information on our development workflow.

Bzlmod support

Status: Beta
Full Feature Parity: No

See Bzlmod support for more details.

Getting started

The following two sections cover using rules_python with bzlmod and the older way of configuring bazel with a WORKSPACE file.

Using bzlmod

IMPORTANT: bzlmod support is still in Beta; APIs are subject to change.

The first step to using rules_python with bzlmod is to add the dependency to your MODULE.bazel file:

# Update the version "0.0.0" to the release found here:
# https://github.com/bazelbuild/rules_python/releases.
bazel_dep(name = "rules_python", version = "0.0.0")

Once added, you can load the rules and use them:

load("@rules_python//python:py_binary.bzl", "py_binary")

py_binary(...)

Depending on what you're doing, you likely want to do some additional configuration to control what Python version is used; read the following sections for how to do that.

Toolchain registration with bzlmod

A default toolchain is automatically configured depending on rules_python. Note, however, the version used tracks the most recent Python release and will change often.

If you want to use a specific Python version for your programs, then how to do so depends on if you're configuring the root module or not. The root module is special because it can set the default Python version, which is used by the version-unaware rules (e.g. //python:py_binary.bzl et al). For submodules, it's recommended to use the version-aware rules to pin your programs to a specific Python version so they don't accidentally run with a different version configured by the root module.

Configuring and using the default Python version

To specify what the default Python version is, set is_default = True when calling python.toolchain(). This can only be done by the root module; it is silently ignored if a submodule does it. Similarly, using the version-unaware rules (which always use the default Python version) should only be done by the root module. If submodules use them, then they may run with a different Python version than they expect.

python = use_extension("@rules_python//python/extensions:python.bzl", "python")

python.toolchain(
    python_version = "3.11",
    is_default = True,
)

Then use the base rules from e.g. //python:py_binary.bzl.

Pinning to a Python version

Pinning to a version allows targets to force that a specific Python version is used, even if the root module configures a different version as a default. This is most useful for two cases:

For submodules to ensure they run with the appropriate Python version
To allow incremental, per-target, upgrading to newer Python versions, typically in a mono-repo situation.

To configure a submodule with the version-aware rules, request the particular version you need, then use the @python_versions repo to use the rules that force specific versions:

python = use_extension("@rules_python//python/extensions:python.bzl", "python")

python.toolchain(
    python_version = "3.11",
)
use_repo(python, "python_versions")

Then use e.g. load("@python_versions//3.11:defs.bzl", "py_binary") to use the rules that force that particular version. Multiple versions can be specified and use within a single build.

For more documentation, see the bzlmod examples under the examples folder. Look for the examples that contain a MODULE.bazel file.

Other toolchain details

The python.toolchain() call makes its contents available under a repo named python_X_Y, where X and Y are the major and minor versions. For example, python.toolchain(python_version="3.11") creates the repo @python_3_11. Remember to call use_repo() to make repos visible to your module: use_repo(python, "python_3_11")

Using a WORKSPACE file

To import rules_python in your project, you first need to add it to your WORKSPACE file, using the snippet provided in the release you choose

To depend on a particular unreleased version, you can do the following:

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")


# Update the SHA and VERSION to the lastest version available here:
# https://github.com/bazelbuild/rules_python/releases.

SHA="84aec9e21cc56fbc7f1335035a71c850d1b9b5cc6ff497306f84cced9a769841"

VERSION="0.23.1"

http_archive(
    name = "rules_python",
    sha256 = SHA,
    strip_prefix = "rules_python-{}".format(VERSION),
    url = "https://github.com/bazelbuild/rules_python/releases/download/{}/rules_python-{}.tar.gz".format(VERSION,VERSION),
)

load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

Toolchain registration

To register a hermetic Python toolchain rather than rely on a system-installed interpreter for runtime execution, you can add to the WORKSPACE file:

load("@rules_python//python:repositories.bzl", "python_register_toolchains")

python_register_toolchains(
    name = "python_3_11",
    # Available versions are listed in @rules_python//python:versions.bzl.
    # We recommend using the same version your team is already standardized on.
    python_version = "3.11",
)

load("@python_3_11//:defs.bzl", "interpreter")

load("@rules_python//python:pip.bzl", "pip_parse")

pip_parse(
    ...
    python_interpreter_target = interpreter,
    ...
)

After registration, your Python targets will use the toolchain's interpreter during execution, but a system-installed interpreter is still used to 'bootstrap' Python targets (see bazelbuild#691). You may also find some quirks while using this toolchain. Please refer to python-build-standalone documentation's Quirks section.

Toolchain usage in other rules

Python toolchains can be utilized in other bazel rules, such as genrule(), by adding the toolchains=["@rules_python//python:current_py_toolchain"] attribute. You can obtain the path to the Python interpreter using the $(PYTHON2) and $(PYTHON3) "Make" Variables. See the test_current_py_toolchain target for an example.

"Hello World"

Once you've imported the rule set into your WORKSPACE using any of these methods, you can then load the core rules in your BUILD files with the following:

load("@rules_python//python:defs.bzl", "py_binary")

py_binary(
  name = "main",
  srcs = ["main.py"],
)

Using dependencies from PyPI

Using PyPI packages (aka "pip install") involves two main steps.

Installing third_party packages
[Using third_party packages as dependencies](#using-third_party-packages-as-dependencies

Installing third_party packages

Using bzlmod

To add pip dependencies to your MODULE.bazel file, use the pip.parse extension, and call it to create the central external repo and individual wheel external repos. Include in the MODULE.bazel the toolchain extension as shown in the first bzlmod example above.

pip = use_extension("@rules_python//python/extensions:pip.bzl", "pip")
pip.parse(
    hub_name = "my_deps",
    python_version = "3.11",
    requirements_lock = "//:requirements_lock_3_11.txt",
)
use_repo(pip, "my_deps")

For more documentation, including how the rules can update/create a requirements file, see the bzlmod examples under the examples folder.

Using a WORKSPACE file

To add pip dependencies to your WORKSPACE, load the pip_parse function and call it to create the central external repo and individual wheel external repos.

load("@rules_python//python:pip.bzl", "pip_parse")

# Create a central repo that knows about the dependencies needed from
# requirements_lock.txt.
pip_parse(
   name = "my_deps",
   requirements_lock = "//path/to:requirements_lock.txt",
)
# Load the starlark macro, which will define your dependencies.
load("@my_deps//:requirements.bzl", "install_deps")
# Call it to define repos for your requirements.
install_deps()

pip rules

Note that since pip_parse is a repository rule and therefore executes pip at WORKSPACE-evaluation time, Bazel has no information about the Python toolchain and cannot enforce that the interpreter used to invoke pip matches the interpreter used to run py_binary targets. By default, pip_parse uses the system command "python3". To override this, pass in the python_interpreter attribute or python_interpreter_target attribute to pip_parse.

You can have multiple pip_parses in the same workspace. Or use the pip extension multiple times when using bzlmod. This configuration will create multiple external repos that have no relation to one another and may result in downloading the same wheels numerous times.

As with any repository rule, if you would like to ensure that pip_parse is re-executed to pick up a non-hermetic change to your environment (e.g., updating your system python interpreter), you can force it to re-execute by running bazel sync --only [pip_parse name].

Note: The pip_install rule is deprecated. pip_parse offers identical functionality, and both pip_install and pip_parse now have the same implementation. The name pip_install may be removed in a future version of the rules.

The maintainers have made all reasonable efforts to facilitate a smooth transition. Still, some users of pip_install will need to replace their existing requirements.txt with a fully resolved set of dependencies using a tool such as pip-tools or the compile_pip_requirements repository rule.

Using third_party packages as dependencies

Each extracted wheel repo contains a py_library target representing the wheel's contents. There are two ways to access this library. The first uses the requirement() function defined in the central repo's //:requirements.bzl file. This function maps a pip package name to a label:

load("@my_deps//:requirements.bzl", "requirement")

py_library(
    name = "mylib",
    srcs = ["mylib.py"],
    deps = [
        ":myotherlib",
        requirement("some_pip_dep"),
        requirement("another_pip_dep"),
    ]
)

The reason requirement() exists is that the pattern for the labels, while not expected to change frequently, is not guaranteed to be stable. Using requirement() ensures you do not have to refactor your BUILD files if the pattern changes.

On the other hand, using requirement() has several drawbacks; see this issue for an enumeration. If you don't want to use requirement(), you can use the library labels directly instead. For pip_parse, the labels are of the following form:

@{name}_{package}//:pkg

Here name is the name attribute that was passed to pip_parse and package is the pip package name with characters that are illegal in Bazel label names (e.g. -, .) replaced with _. If you need to update name from "old" to "new", then you can run the following buildozer command:

buildozer 'substitute deps @old_([^/]+)//:pkg @new_${1}//:pkg' //...:*

For pip_install, the labels are instead of the form:

@{name}//pypi__{package}

'Extras' dependencies

Any 'extras' specified in the requirements lock file will be automatically added as transitive dependencies of the package. In the example above, you'd just put requirement("useful_dep").

Consuming Wheel Dists Directly

If you need to depend on the wheel dists themselves, for instance, to pass them to some other packaging tool, you can get a handle to them with the whl_requirement macro. For example:

filegroup(
    name = "whl_files",
    data = [
        whl_requirement("boto3"),
    ]
)

Python Gazelle plugin

Gazelle is a build file generator for Bazel projects. It can create new BUILD.bazel files for a project that follows language conventions and update existing build files to include new sources, dependencies, and options.

Bazel may run Gazelle using the Gazelle rule, or it may be installed and run as a command line tool.

See the documentation for Gazelle with rules_python here.

Migrating from the bundled rules

The core rules are currently available in Bazel as built-in symbols, but this form is deprecated. Instead, you should depend on rules_python in your WORKSPACE file and load the Python rules from @rules_python//python:defs.bzl.

A buildifier fix is available to automatically migrate BUILD and .bzl files to add the appropriate load() statements and rewrite uses of native.py_*.

# Also consider using the -r flag to modify an entire workspace.
buildifier --lint=fix --warnings=native-py <files>

Currently, the WORKSPACE file needs to be updated manually as per Getting started above.

Note that Starlark-defined bundled symbols underneath @bazel_tools//tools/python are also deprecated. These are not yet rewritten by buildifier.

mortenmj/rules_python

Python Rules for Bazel

Overview

Bzlmod support

Getting started

Using bzlmod

Toolchain registration with bzlmod

Configuring and using the default Python version

Pinning to a Python version

Other toolchain details

Using a WORKSPACE file

Toolchain registration

Toolchain usage in other rules

"Hello World"

Using dependencies from PyPI

Installing third_party packages

Using bzlmod

Using a WORKSPACE file

pip rules

Using third_party packages as dependencies

'Extras' dependencies

Consuming Wheel Dists Directly

Python Gazelle plugin

Migrating from the bundled rules