Configuring Python toolchains and runtimes — rules_python 0.0.0 documentation (original) (raw)

This documents how to configure the Python toolchain and runtimes for different use cases.

Bzlmod MODULE configuration

How to configure rules_python in your MODULE.bazel file depends on how and why you’re using Python. There are 4 basic use cases:

  1. A root module that always uses Python. For example, you’re building a Python application.
  2. A library module with dev-only uses of Python. For example, a Java project that only uses Python as part of testing itself.
  3. A library module without version constraints. For example, a rule set with Python build tools, but defers to the user as to what Python version is used for the tools.
  4. A library module with version constraints. For example, a rule set with Python build tools, and the module requires a specific version of Python be used with its tools.

Root modules

Root modules are always the top-most module. These are special in two ways:

  1. Some rules_python bzlmod APIs are only respected by the root module.
  2. The root module can force module overrides and specific module dependency ordering.

When configuring rules_python for a root module, you typically want to explicitly specify the Python version you want to use. This ensures that dependencies don’t change the Python version out from under you. Remember thatrules_python will set a version by default, but it will change regularly as it tracks a recent Python version.

NOTE: If your root module only uses Python for development of the module itself, you should read the dev-only library module section.

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

python.toolchain(python_version = "3.12", is_default = True)

Library modules

A library module is a module that can show up in arbitrary locations in the bzlmod module graph – it’s unknown where in the breadth-first search order the module will be relative to other modules. For example, rules_python is a library module.

Library modules with dev-only Python usage

A library module with dev-only Python usage is usually one where Python is only used as part of its tests. For example, a module for Java rules might run some Python program to generate test data, but real usage of the rules don’t need Python to work. To configure this, follow the root-module setup, but remember to specify dev_dependency = True to the bzlmod APIs:

MODULE.bazel

bazel_dep(name = "rules_python", version=..., dev_dependency = True)

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

python.toolchain(python_version = "3.12", is_default=True)

Library modules without version constraints

A library module without version constraints is one where the version of Python used for the Python programs it runs isn’t chosen by the module itself. Instead, it’s up to the root module to pick an appropriate version of Python.

For this case, configuration is simple: just depend on rules_python and use the normal //python:py_binary.bzl et al rules. There is no need to callpython.toolchain – rules_python ensures some Python version is available, but more often the root module will specify some version.

MODULE.bazel

bazel_dep(name = "rules_python", version=...)

Library modules with version constraints

A library module with version constraints is one where the module requires a specific Python version be used with its tools. This has some pros/cons:

To configure this, request the Python versions needed in MODULE.bazel and use the version-aware rules for py_binary.

MODULE.bazel

bazel_dep(name = "rules_python", version=...)

python = use_extension("@rules_python//python/extensions:python.bzl", "python") python.toolchain(python_version = "3.12")

BUILD.bazel

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

py_binary(..., python_version="3.12")

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:

  1. For submodules to ensure they run with the appropriate Python version
  2. 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 when defining the toolchain:

MODULE.bazel

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

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

Then use the @rules_python repo in your BUILD file to explicity pin the Python version when calling the rule:

BUILD.bazel

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

py_binary(..., python_version = "3.11") py_test(..., python_version = "3.11")

Multiple versions can be specified and used within a single build.

MODULE.bazel

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

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

python.toolchain( python_version = "3.12", )

BUILD.bazel

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

Defaults to 3.11

py_binary(...) py_test(...)

Explicitly use Python 3.11

py_binary(..., python_version = "3.11") py_test(..., python_version = "3.11")

Explicitly use Python 3.12

py_binary(..., python_version = "3.12") py_test(..., python_version = "3.12")

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

Other toolchain details

The python.toolchain() call makes its contents available under a repo namedpython_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")

Deprecated since version 1.1.0: The toolchain specific py_binary and py_test symbols are aliases to the regular rules. i.e. Deprecated load("@python_versions//3.11:defs.bzl", "py_binary") & load("@python_versions//3.11:defs.bzl", "py_test")

Usages of them should be changed to load the regular rules directly; i.e. Use load("@rules_python//python:py_binary.bzl", "py_binary") & load("@rules_python//python:py_test.bzl", "py_test") and then specify the python_version when using the rules corresponding to the python version you defined in your toolchain. Library modules with version constraints

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 thetest_current_py_toolchain target for an example.

Overriding toolchain defaults and adding more versions

One can perform various overrides for the registered toolchains from the root module. For example, the following use cases would be supported using the existing attributes:

Using defined toolchains from WORKSPACE

It is possible to use toolchains defined in MODULE.bazel in WORKSPACE. For example the following MODULE.bazel and WORKSPACE provides a working pip_parse setup:

File: WORKSPACE

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

py_repositories()

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

pip_parse( name = "third_party", requirements_lock = "//:requirements.txt", python_interpreter_target = "@python_3_10_host//:python", )

load("@third_party//:requirements.bzl", "install_deps")

install_deps()

File: MODULE.bazel

bazel_dep(name = "rules_python", version = "0.40.0")

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

python.toolchain(is_default = True, python_version = "3.10")

use_repo(python, "python_3_10", "python_3_10_host")

Note, the user has to import the *_host repository to use the python interpreter in thepip_parse and whl_library repository rules and once that is done users should be able to ensure the setting of the default toolchain even during the transition period when some of the code is still defined in WORKSPACE.

Workspace configuration

To import rules_python in your project, you first need to add it to yourWORKSPACE file, using the snippet provided in therelease 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()

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

pip_parse( ... python_interpreter_target = "@python_3_11_host//:python", ... )

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 https://github.com/bazelbuild/rules\_python/issues/691). You may also find some quirks while using this toolchain. Please refer to python-build-standalone documentation’s Quirks section.

Autodetecting toolchain

The autodetecting toolchain is a deprecated toolchain that is built into Bazel. It’s name is a bit misleading: it doesn’t autodetect anything. All it does is use python3 from the environment a binary runs within. This provides extremely limited functionality to the rules (at build time, nothing is knowable about the Python runtime).

Bazel itself automatically registers @bazel_tools//tools/python:autodetecting_toolchainas the lowest priority toolchain. For WORKSPACE builds, if no other toolchain is registered, that toolchain will be used. For bzlmod builds, rules_python automatically registers a higher-priority toolchain; it won’t be used unless there is a toolchain misconfiguration somewhere.

To aid migration off the Bazel-builtin toolchain, rules_python provides@rules_python//python/runtime_env_toolchains:all. This is an equivalent toolchain, but is implemented using rules_python’s objects.

Custom toolchains

While rules_python provides toolchains by default, it is not required to use them, and you can define your own toolchains to use instead. This section gives an introduction for how to define them yourself.

Note

Under the hood, there are multiple toolchains that comprise the different information necessary to build Python targets. Each one has an associated toolchain type that identifies it. We call the collection of these toolchains a “toolchain suite”.

One of the underlying design goals of the toolchains is to support complex and bespoke environments. Such environments may use an arbitrary combination ofRBE, cross-platform building, multiple Python versions, building Python from source, embeding Python (as opposed to building separate interpreters), using prebuilt binaries, or using binaries built from source. To that end, many of the attributes they accept, and fields they provide, are optional.

Target toolchain type

The target toolchain type is //python:toolchain_type, and it is for target configuration runtime information, e.g., the Python version and interpreter binary that a program will use.

The is typically implemented using py_runtime(), which provides the PyRuntimeInfo provider. For historical reasons from the Python 2 transition, py_runtime is wrapped in py_runtime_pair, which provides ToolchainInfo with the field py3_runtime, which is an instance of PyRuntimeInfo.

This toolchain type is intended to hold only target configuration values. As such, when defining its associated toolchain target, only set toolchain.target_compatible_with and/ortoolchain.target_settings constraints; there is no need to set toolchain.exec_compatible_with.

Python C toolchain type

The Python C toolchain type (“py cc”) is //python/cc:toolchain_type, and it has C/C++ information for the target configuration, e.g. the C headers that provide Python.h.

This is typically implemented using py_cc_toolchain(), which providesToolchainInfo with the field py_cc_toolchain set, which is aPyCcToolchainInfo provider instance.

This toolchain type is intended to hold only target configuration values relating to the C/C++ information for the Python runtime. As such, when defining its associated toolchain target, only settoolchain.target_compatible_with and/ortoolchain.target_settings constraints; there is no need to set toolchain.exec_compatible_with.

Exec tools toolchain type

The exec tools toolchain type is //python:exec_tools_toolchain_type, and it is for supporting tools for building programs, e.g. the binary to precompile code at build time.

This toolchain type is intended to hold only exec configuration values – usually tools (prebuilt or from-source) used to build Python targets.

This is typically implemented using py_exec_tools_toolchain, which provides ToolchainInfo with the field exec_tools set, which is an instance of PyExecToolsInfo.

The toolchain constraints of this toolchain type can be a bit more nuanced than the other toolchain types. Typically, you settoolchain.target_settings to the Python version the tools are for, and toolchain.exec_compatible_with to the platform they can run on. This allows the toolchain to first be considered based on the target configuration (e.g. Python version), then for one to be chosen based on finding one compatible with the available host platforms to run the tool on.

However, what target_compatible_with/target_settings andexec_compatible_with values to use depend on details of the tools being used. For example:

This can work because, when the rules invoke these build tools, they pass along all necessary information so that the tool can be entirely independent of the target configuration being built for.

Alternatively, if you had a precompiler that only ran on linux, and only produced valid output for programs intended to run on linux, then both exec_compatible_with and target_compatible_with must be set to linux.

Custom toolchain example

Here, we show an example for a semi-complicated toolchain suite, one that is:

Defining toolchains for this might look something like this:

-------------------------------------------------------

File: toolchain_impl/BUILD

Contains the tool definitions (runtime, headers, libs).

-------------------------------------------------------

load("@rules_python//python:py_cc_toolchain.bzl", "py_cc_toolchain") load("@rules_python//python:py_exec_tools_toolchain.bzl", "py_exec_tools_toolchain") load("@rules_python//python:py_runtime.bzl", "py_runtime") load("@rules_python//python:py_runtime_pair.bzl", "py_runtime_pair")

MAJOR = 3 MINOR = 12 MICRO = 0

py_runtime( name = "runtime", interpreter = ":python", interpreter_version_info = { "major": str(MAJOR), "minor": str(MINOR), "micro": str(MICRO), } implementation = "cpython" ) py_runtime_pair( name = "runtime_pair", py3_runtime = ":runtime" )

py_cc_toolchain( name = "py_cc_toolchain_impl", headers = ":headers", libs = ":libs", python_version = "{}.{}".format(MAJOR, MINOR) )

py_exec_tools_toolchain( name = "exec_tools_toolchain_impl", exec_interpreter = "@rules_python/python:none", precompiler = "precompiler-cpython-3.12" )

cc_binary(name = "python3.12", ...) cc_library(name = "headers", ...) cc_library(name = "libs", ...)

------------------------------------------------------------------

File: toolchains/BUILD

Putting toolchain() calls in a separate package from the toolchain

implementations minimizes Bazel loading overhead.

------------------------------------------------------------------

toolchain( name = "runtime_toolchain", toolchain = "//toolchain_impl:runtime_pair", toolchain_type = "@rules_python//python:toolchain_type", target_compatible_with = ["@platforms/os:linux"] ) toolchain( name = "py_cc_toolchain", toolchain = "//toolchain_impl:py_cc_toolchain_impl", toolchain_type = "@rules_python//python/cc:toolchain_type", target_compatible_with = ["@platforms/os:linux"] )

toolchain( name = "exec_tools_toolchain", toolchain = "//toolchain_impl:exec_tools_toolchain_impl", toolchain_type = "@rules_python//python:exec_tools_toolchain_type", target_settings = [ "@rules_python//python/config_settings:is_python_3.12", ], exec_comaptible_with = ["@platforms/os:linux"] )

-----------------------------------------------

File: MODULE.bazel or WORKSPACE.bazel

These toolchains will considered before others.

-----------------------------------------------

register_toolchains("//toolchains:all")

When registering custom toolchains, be aware of the the toolchain registration order. In brief, toolchain order is the BFS-order of the modules; see the bazel docs for a more detailed description.

Note

The toolchain() calls should be in a separate BUILD file from everything else. This avoids Bazel having to perform unnecessary work when it discovers the list of available toolchains.

Toolchain selection flags

Currently the following flags are used to influence toolchain selection: