docs/toolchains.md - third_party/github.com/bazelbuild/rules_python - Git at Google

 :::{default-domain} bzl
 :::

 # Configuring Python toolchains and runtimes

 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 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.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 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("@python_versions//3.12:defs.bzl", "py_binary")

 py_binary(...)
 ```

 ### 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, then use the `@python_versions` repo to use the rules that
 force specific versions:

 ```starlark
 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 {gh-path}`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")`

 #### 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](https://bazel.build/reference/be/make-variables). See the
 {gh-path}`test_current_py_toolchain <tests/load_from_macro/BUILD.bazel>` 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:

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

 ### 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 {bzl:obj}`pip_parse` setup:
 ```starlark
 # 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 the
 {bzl:obj}`pip_parse` and {bzl:obj}`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](https://github.com/bazelbuild/rules_python/releases)

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

 ```starlark
 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:

 ```starlark
 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](https://gregoryszorc.com/docs/python-build-standalone/main/quirks.html).

 ## 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_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
 {bzl:obj}`@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}
 * 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
 {obj}`RBE`, 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 {obj}`//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 {obj}`py_runtime()`, which
 provides the {obj}`PyRuntimeInfo` provider. For historical reasons from the
 Python 2 transition, `py_runtime` is wrapped in {obj}`py_runtime_pair`,
 which provides {obj}`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 {external:bzl:obj}`toolchain` target, only
 set {external:bzl:obj}`toolchain.target_compatible_with` and/or
 {external:bzl:obj}`toolchain.target_settings` constraints; there is no need to
 set {external:bzl:obj}`toolchain.exec_compatible_with`.

 ### Python C toolchain type

 The Python C toolchain type ("py cc") is {obj}`//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 {obj}`py_cc_toolchain()`, which provides
 {obj}`ToolchainInfo` with the field `py_cc_toolchain` set, which is a
 {obj}`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 {external:obj}`toolchain` target, only set
 {external:bzl:obj}`toolchain.target_compatible_with` and/or
 {external:bzl:obj}`toolchain.target_settings` constraints; there is no need to
 set {external:bzl:obj}`toolchain.exec_compatible_with`.

 ### Exec tools toolchain type

 The exec tools toolchain type is {obj}`//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 {obj}`py_exec_tools_toolchain`, which
 provides {obj}`ToolchainInfo` with the field `exec_tools` set, which is an
 instance of {obj}`PyExecToolsInfo`.

 The toolchain constraints of this toolchain type can be a bit more nuanced than
 the other toolchain types. Typically, you set
 {external:bzl:obj}`toolchain.target_settings` to the Python version the tools
 are for, and {external:bzl:obj}`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 depend on 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 byte
   code valid for 3.12
 * With the exec tools interpreter disabled (unnecessary with a prebuild
   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_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](https://bazel.build/extending/toolchains#toolchain-resolution). 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:
 * {obj}`--@rules_python//python/config_settings:py_linux_libc` for selecting the Linux libc variant.
 * {obj}`--@rules_python//python/config_settings:py_freethreaded` for selecting
   the freethreaded experimental Python builds available from `3.13.0` onwards.
	:::{default-domain} bzl
	:::

	# Configuring Python toolchains and runtimes

	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 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.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 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("@python_versions//3.12:defs.bzl", "py_binary")

	py_binary(...)
	```

	### 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, then use the `@python_versions` repo to use the rules that
	force specific versions:

	```starlark
	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 {gh-path}`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")`

	#### 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](https://bazel.build/reference/be/make-variables). See the
	{gh-path}`test_current_py_toolchain <tests/load_from_macro/BUILD.bazel>` 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:

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

	### 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 {bzl:obj}`pip_parse` setup:
	```starlark
	# 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 the
	{bzl:obj}`pip_parse` and {bzl:obj}`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](https://github.com/bazelbuild/rules_python/releases)

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

	```starlark
	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:

	```starlark
	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](https://gregoryszorc.com/docs/python-build-standalone/main/quirks.html).

	## 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_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
	{bzl:obj}`@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}
	* 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
	{obj}`RBE`, 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 {obj}`//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 {obj}`py_runtime()`, which
	provides the {obj}`PyRuntimeInfo` provider. For historical reasons from the
	Python 2 transition, `py_runtime` is wrapped in {obj}`py_runtime_pair`,
	which provides {obj}`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 {external:bzl:obj}`toolchain` target, only
	set {external:bzl:obj}`toolchain.target_compatible_with` and/or
	{external:bzl:obj}`toolchain.target_settings` constraints; there is no need to
	set {external:bzl:obj}`toolchain.exec_compatible_with`.

	### Python C toolchain type

	The Python C toolchain type ("py cc") is {obj}`//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 {obj}`py_cc_toolchain()`, which provides
	{obj}`ToolchainInfo` with the field `py_cc_toolchain` set, which is a
	{obj}`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 {external:obj}`toolchain` target, only set
	{external:bzl:obj}`toolchain.target_compatible_with` and/or
	{external:bzl:obj}`toolchain.target_settings` constraints; there is no need to
	set {external:bzl:obj}`toolchain.exec_compatible_with`.

	### Exec tools toolchain type

	The exec tools toolchain type is {obj}`//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 {obj}`py_exec_tools_toolchain`, which
	provides {obj}`ToolchainInfo` with the field `exec_tools` set, which is an
	instance of {obj}`PyExecToolsInfo`.

	The toolchain constraints of this toolchain type can be a bit more nuanced than
	the other toolchain types. Typically, you set
	{external:bzl:obj}`toolchain.target_settings` to the Python version the tools
	are for, and {external:bzl:obj}`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 depend on 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 byte
	code valid for 3.12
	* With the exec tools interpreter disabled (unnecessary with a prebuild
	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_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](https://bazel.build/extending/toolchains#toolchain-resolution). 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:
	* {obj}`--@rules_python//python/config_settings:py_linux_libc` for selecting the Linux libc variant.
	* {obj}`--@rules_python//python/config_settings:py_freethreaded` for selecting
	the freethreaded experimental Python builds available from `3.13.0` onwards.