Configuring Python toolchains and runtimes

This document explains 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 four basic use cases:

A root module that always uses Python. For example, you’re building a Python application.
A library module with dev-only uses of Python. For example, a Java project that only uses Python as part of testing itself.
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.
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:

Some rules_python bzlmod APIs are only respected by the root module.
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 that rules_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.defaults(python_version = "3.12")
python.toolchain(python_version = "3.12")

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.defaults(python_version = "3.12")
python.toolchain(python_version = "3.12")

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 call python.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:

It allows the library’s tools to use a different version of Python than the rest of the build. For example, a user’s program could use Python 3.12, while the library module’s tools use Python 3.10.
It reduces the support burden for the library module because the library only needs to test for the particular Python version they intend to run as.
It raises the support burden for the library module because the version of Python being used needs to be regularly incremented.
It has higher build overhead because additional runtimes and libraries need to be downloaded, and Bazel has to keep additional configuration state.

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:

For submodules to ensure they run with the appropriate Python version
To allow incremental, per-target, upgrading to newer Python versions, typically in a monorepo 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 explicitly 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.defaults(
    # The environment variable takes precedence if set.
    python_version = "3.11",
    python_version_env = "BAZEL_PYTHON_VERSION",
)
python.toolchain(
    python_version = "3.11",
)

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 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").

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

Usages of them should be changed to load the regular rules directly. For example, 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 the test_current_py_toolchain target for an example. We also make available $(PYTHON2_ROOTPATH) and $(PYTHON3_ROOTPATH), which are Make Variable equivalents of $(PYTHON2) and $(PYTHON3) but for runfiles locations. These will be helpful if you need to set environment variables of binary/test rules while using --nolegacy_external_runfiles. The original make variables still work in exec contexts such as genrules.

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:

Limiting the available toolchains for the entire bzlmod transitive graph via python.override.available_python_versions.
Setting particular X.Y.Z Python versions when modules request X.Y version via python.override.minor_mapping.
Per-version control of the coverage tool used using python.single_version_platform_override.coverage_tool.
Adding additional Python versions via python.single_version_override or python.single_version_platform_override.

Registering custom runtimes

Because the python-build-standalone project has thousands of prebuilt runtimes available, rules_python only includes popular runtimes in its built-in configurations. If you want to use a runtime that isn’t already known to rules_python, then single_version_platform_override() can be used to do so. In short, it allows specifying an arbitrary URL and using custom flags to control when a runtime is used.

In the example below, we register a particular python-build-standalone runtime that is activated for Linux x86 builds when the custom flag --//:runtime=my-custom-runtime is set.

# File: MODULE.bazel
bazel_dep(name = "bazel_skylib", version = "1.7.1.")
bazel_dep(name = "rules_python", version = "1.5.0")
python = use_extension("@rules_python//python/extensions:python.bzl", "python")
python.single_version_platform_override(
    platform = "my-platform",
    python_version = "3.13.3",
    sha256 = "01d08b9bc8a96698b9d64c2fc26da4ecc4fa9e708ce0a34fb88f11ab7e552cbd",
    os_name = "linux",
    arch = "x86_64",
    target_settings = [
        "@@//:runtime=my-custom-runtime",
    ],
    urls = ["https://github.com/astral-sh/python-build-standalone/releases/download/20250409/cpython-3.13.3+20250409-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"],
)
# File: //:BUILD.bazel
load("@bazel_skylib//rules:common_settings.bzl", "string_flag")
string_flag(
    name = "custom_runtime",
    build_setting_default = "",
)
config_setting(
    name = "is_custom_runtime_linux-x86-install-only-stripped",
    flag_values = {
        ":custom_runtime": "linux-x86-install-only-stripped",
    },
)

Notes:

While any URL and archive can be used, it’s assumed their content looks like a python-build-standalone archive.
A “version-aware” toolchain is registered, which means the Python version flag must also match (e.g., --@rules_python//python/config_settings:python_version=3.13.3 must be set – see minor_mapping and is_default for controls and docs about version matching and selection).
The target_compatible_with attribute can be used to entirely specify the argument of the same name that the toolchain uses.
The labels in target_settings must be absolute; @@ refers to the main repo.
The target_settings are config_setting targets, which means you can customize how matching occurs.

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.defaults(python_version = "3.10")
python.toolchain(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 the pip_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 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/bazel-contrib/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/bazel-contrib/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/bazel-contrib/rules_python/issues/691). You may also find some quirks while using this toolchain. Please refer to python-build-standalone documentation’s Quirks section.

Local toolchain

It’s possible to use a locally installed Python runtime instead of the regular prebuilt, remotely downloaded ones. A local toolchain contains the Python runtime metadata (Python version, headers, ABI flags, etc.) that the regular remotely downloaded runtimes contain, which makes it possible to build, e.g., C extensions (unlike the autodetecting and runtime environment toolchains).

For simple cases, the local_runtime_repo and local_runtime_toolchains_repo rules are provided that will introspect a Python installation and create an appropriate Bazel definition from it. To do this, three pieces need to be wired together:

Specify a path or command to a Python interpreter (multiple can be defined).
Create toolchains for the runtimes in (1).
Register the toolchains created by (2).

The following is an example that will use python3 from PATH to find the interpreter, then introspect its installation to generate a full toolchain.

# File: MODULE.bazel

local_runtime_repo = use_repo_rule(
    "@rules_python//python/local_toolchains:repos.bzl",
    "local_runtime_repo",
    dev_dependency = True,
)

local_runtime_toolchains_repo = use_repo_rule(
    "@rules_python//python/local_toolchains:repos.bzl",
    "local_runtime_toolchains_repo",
    dev_dependency = True,
)

# Step 1: Define the Python runtime
local_runtime_repo(
    name = "local_python3",
    interpreter_path = "python3",
    on_failure = "fail",
)

# Step 2: Create toolchains for the runtimes
local_runtime_toolchains_repo(
    name = "local_toolchains",
    runtimes = ["local_python3"],
    # TIP: The `target_settings` arg can be used to activate them based on
    # command line flags; see docs below.
)

# Step 3: Register the toolchains
register_toolchains("@local_toolchains//:all", dev_dependency = True)

Important

Be sure to set dev_dependency = True. Using a local toolchain only makes sense for the root module.

If an intermediate module does it, then the register_toolchains() call will take precedence over the default rules_python toolchains and cause problems for downstream modules.

Multiple runtimes and/or toolchains can be defined, which allows for multiple Python versions and/or platforms to be configured in a single MODULE.bazel. Note that register_toolchains will insert the local toolchain earlier in the toolchain ordering, so it will take precedence over other registered toolchains. To better control when the toolchain is used, see [Conditionally using local toolchains].

Conditionally using local toolchains

By default, a local toolchain has few constraints and is early in the toolchain ordering, which means it will usually be used no matter what. This can be problematic for CI (where it shouldn’t be used), expensive for CI (CI must initialize/download the repository to determine its Python version), and annoying for iterative development (enabling/disabling it requires modifying MODULE.bazel).

These behaviors can be mitigated, but it requires additional configuration to avoid triggering the local toolchain repository to initialize (i.e., run local commands and perform downloads).

The two settings to change are local_runtime_toolchains_repo.target_compatible_with and local_runtime_toolchains_repo.target_settings, which control how Bazel decides if a toolchain should match. By default, they point to targets within the local runtime repository (triggering repo initialization). We have to override them to not reference the local runtime repository at all.

In the example below, we reconfigure the local toolchains so they are only activated if the custom flag --//:py=local is set and the target platform matches the Bazel host platform. The net effect is that CI won’t use the local toolchain (nor initialize its repository), and developers can easily enable/disable the local toolchain with a command line flag.

# File: MODULE.bazel
bazel_dep(name = "bazel_skylib", version = "1.7.1")

local_runtime_toolchains_repo(
    name = "local_toolchains",
    runtimes = ["local_python3"],
    target_compatible_with = {
        "local_python3": ["HOST_CONSTRAINTS"],
    },
    target_settings = {
        "local_python3": ["@//:is_py_local"]
    }
)

# File: BUILD.bazel
load("@bazel_skylib//rules:common_settings.bzl", "string_flag")

config_setting(
    name = "is_py_local",
    flag_values = {":py": "local"},
)

string_flag(
    name = "py",
    build_setting_default = "",
)

Tip

Easily switching between multiple local toolchains can be accomplished by adding additional :is_py_X targets and setting --//:py to match. to easily switch between different local toolchains.

Runtime environment toolchain

The runtime environment toolchain is a minimal toolchain that doesn’t provide information about Python at build time. In particular, this means it is not able to build C extensions – doing so requires knowing, at build time, what Python headers to use.

In effect, all it does is generate a small wrapper script that simply calls, e.g., /usr/bin/env python3 to run a program. This makes it easy to change what Python is used to run a program but also makes it easy to use a Python version that isn’t compatible with build-time assumptions.

register_toolchains("@rules_python//python/runtime_env_toolchains:all")

Note that this toolchain has no constraints, i.e. it will match any platform, Python version, etc.

Autodetecting toolchain

The autodetecting toolchain is a deprecated toolchain that is built into Bazel. Its 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_toolchain as 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 to how to define them yourself.

Note

Defining your own toolchains is an advanced feature.
APIs used for defining them are less stable and may change more often.

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 of RBE, cross-platform building, multiple Python versions, building Python from source, embedding 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.

This 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/or toolchain.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 provides ToolchainInfo with the field py_cc_toolchain set, which is a PyCcToolchainInfo 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 set toolchain.target_compatible_with and/or toolchain.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 set toolchain.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 and exec_compatible_with values to use depends on the details of the tools being used. For example:

If you had a precompiler that supported any version of Python, then putting the Python version in target_settings is unnecessary.
If you had a prebuilt polyglot precompiler binary that could run on any platform, then setting exec_compatible_with is unnecessary.

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:

A CPython-based interpreter
For Python version 3.12.0
Using an in-build interpreter built from source
That only runs on Linux
Using a prebuilt precompiler that only runs on Linux and only produces bytecode valid for 3.12
With the exec tools interpreter disabled (unnecessary with a prebuilt precompiler)
Providing C headers and libraries

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_compatible_with = ["@platforms/os:linux"],
)

# -----------------------------------------------
# File: MODULE.bazel or WORKSPACE.bazel
# These toolchains will be considered before others.
# -----------------------------------------------
register_toolchains("//toolchains:all")

When registering custom toolchains, be aware of 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:

--@rules_python//python/config_settings:py_linux_libc for selecting the Linux libc variant.
--@rules_python//python/config_settings:py_freethreaded for selecting the freethreaded experimental Python builds available from 3.13.0 onwards.

Running the underlying interpreter

To run the interpreter that Bazel will use, you can use the @rules_python//python/bin:python target. This is a binary target with the executable pointing at the python3 binary plus its relevant runfiles.

$ bazel run @rules_python//python/bin:python
Python 3.11.1 (main, Jan 16 2023, 22:41:20) [Clang 15.0.7 ] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
$ bazel run @rules_python//python/bin:python --@rules_python//python/config_settings:python_version=3.12
Python 3.12.0 (main, Oct  3 2023, 01:27:23) [Clang 17.0.1 ] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

You can also access a specific binary’s interpreter this way by using the @rules_python//python/bin:python_src target. In the example below, it is assumed that the @rules_python//tools/publish:twine binary is fixed at Python 3.11.

$ bazel run @rules_python//python/bin:python --@rules_python//python/bin:interpreter_src=@rules_python//tools/publish:twine
Python 3.11.1 (main, Jan 16 2023, 22:41:20) [Clang 15.0.7 ] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
$ bazel run @rules_python//python/bin:python --@rules_python//python/bin:interpreter_src=@rules_python//tools/publish:twine --@rules_python//python/config_settings:python_version=3.12
Python 3.11.1 (main, Jan 16 2023, 22:41:20) [Clang 15.0.7 ] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

Despite setting the Python version explicitly to 3.12 in the example above, the interpreter comes from the @rules_python//tools/publish:twine binary. That is a fixed version.

Note

The python target does not provide access to any modules from py_* targets on its own. Please file a feature request if this is desired.

Differences from `//python/bin:repl`

The //python/bin:python target provides access to the underlying interpreter without any hermeticity guarantees.

The //python/bin:repl target provides an environment identical to what py_binary provides. That means it handles things like the PYTHONSAFEPATH environment variable automatically. The //python/bin:python target will not.

Configuring Python toolchains and runtimes

Bzlmod MODULE configuration

Root modules

Library modules

Library modules with dev-only Python usage

Library modules without version constraints

Library modules with version constraints

Pinning to a Python version

Other toolchain details

Toolchain usage in other rules

Overriding toolchain defaults and adding more versions

Registering custom runtimes

Using defined toolchains from WORKSPACE

Workspace configuration

Workspace toolchain registration

Local toolchain

Conditionally using local toolchains

Runtime environment toolchain

Autodetecting toolchain

Custom toolchains

Target toolchain type

Python C toolchain type

Exec tools toolchain type

Custom toolchain example

Toolchain selection flags

Running the underlying interpreter

Differences from //python/bin:repl

Differences from `//python/bin:repl`