:::{default-domain} bzl :::
Using PyPI packages (aka “pip install”) involves two main steps.
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 {gh-path}examples folder or the documentation for the {obj}@rules_python//python/extensions:pip.bzl extension.
We are using a host-platform compatible toolchain by default to setup pip dependencies. During the setup phase, we create some symlinks, which may be inefficient on Windows by default. In that case use the following `.bazelrc` options to improve performance if you have admin privileges: startup --windows_enable_symlinks This will enable symlinks on Windows and help with bootstrap performance of setting up the hermetic host python interpreter on this platform. Linux and OSX users should see no difference.
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()
(vendoring-requirements)=
In some cases you may not want to generate the requirements.bzl file as a repository rule while Bazel is fetching dependencies. For example, if you produce a reusable Bazel module such as a ruleset, you may want to include the requirements.bzl file rather than make your users install the WORKSPACE setup to generate it. See https://github.com/bazelbuild/rules_python/issues/608
This is the same workflow as Gazelle, which creates go_repository rules with update-repos
To do this, use the “write to source file” pattern documented in https://blog.aspect.dev/bazel-can-write-to-the-source-folder to put a copy of the generated requirements.bzl into your project. Then load the requirements.bzl file directly rather than from the generated repository. See the example in rules_python/examples/pip_parse_vendored.
(per-os-arch-requirements)=
In some cases you may need to use different requirements files for different OS, Arch combinations. This is enabled via the requirements_by_platform attribute in pip.parse extension and the pip_parse repository rule. The keys of the dictionary are labels to the file and the values are a list of comma separated target (os, arch) tuples.
For example:
# ... requirements_by_platform = { "requirements_linux_x86_64.txt": "linux_x86_64", "requirements_osx.txt": "osx_*", "requirements_linux_exotic.txt": "linux_exotic", "requirements_some_platforms.txt": "linux_aarch64,windows_*", }, # For the list of standard platforms that the rules_python has toolchains for, default to # the following requirements file. requirements_lock = "requirements_lock.txt",
In case of duplicate platforms, rules_python will raise an error as there has to be unambiguous mapping of the requirement files to the (os, arch) tuples.
An alternative way is to use per-OS requirement attributes.
# ... requirements_windows = "requirements_windows.txt", requirements_darwin = "requirements_darwin.txt", # For the remaining platforms (which is basically only linux OS), use this file. requirements_lock = "requirements_lock.txt", )
Note that since pip_parse and pip.parse are executed at 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. The pip.parse bzlmod extension by default uses the hermetic python toolchain for the host platform.
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].
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 to insulate from changes to the underlying repository and label strings. However, those labels have become directly used, so aren't able to easily change regardless.
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}
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//([^/]+) @new//${1}' //...:*
If you would like to access entry points, see the py_console_script_binary rule documentation, which can help you create a py_binary target for a particular console script exposed by a package.
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") or @pypi//useful_dep.
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:
load("@pypi//:requirements.bzl", "whl_requirement") filegroup( name = "whl_files", data = [ # This is equivalent to "@pypi//boto3:whl" whl_requirement("boto3"), ] )
The rule {obj}whl_filegroup exists as an easy way to extract the necessary files from a whl file without the need to modify the BUILD.bazel contents of the whl repositories generated via pip_repository. Use it similarly to the filegroup above. See the API docs for more information.
(advance-topics)=
(circular-deps)=
Sometimes PyPi packages contain dependency cycles -- for instance a particular version sphinx (this is no longer the case in the latest version as of 2024-06-02) depends on sphinxcontrib-serializinghtml. When using them as requirement()s, ala
py_binary(
name = "doctool",
...
deps = [
requirement("sphinx"),
],
)
Bazel will protest because it doesn't support cycles in the build graph --
ERROR: .../external/pypi_sphinxcontrib_serializinghtml/BUILD.bazel:44:6: in alias rule @pypi_sphinxcontrib_serializinghtml//:pkg: cycle in dependency graph:
//:doctool (...)
@pypi//sphinxcontrib_serializinghtml:pkg (...)
.-> @pypi_sphinxcontrib_serializinghtml//:pkg (...)
| @pypi_sphinxcontrib_serializinghtml//:_pkg (...)
| @pypi_sphinx//:pkg (...)
| @pypi_sphinx//:_pkg (...)
`-- @pypi_sphinxcontrib_serializinghtml//:pkg (...)
The experimental_requirement_cycles argument allows you to work around these issues by specifying groups of packages which form cycles. pip_parse will transparently fix the cycles for you and provide the cyclic dependencies simultaneously.
pip_parse( ... experimental_requirement_cycles = { "sphinx": [ "sphinx", "sphinxcontrib-serializinghtml", ] }, )
pip_parse supports fixing multiple cycles simultaneously, however cycles must be distinct. apache-airflow for instance has dependency cycles with a number of its optional dependencies, which means those optional dependencies must all be a part of the airflow cycle. For instance --
pip_parse( ... experimental_requirement_cycles = { "airflow": [ "apache-airflow", "apache-airflow-providers-common-sql", "apache-airflow-providers-postgres", "apache-airflow-providers-sqlite", ] } )
Alternatively, one could resolve the cycle by removing one leg of it.
For example while apache-airflow-providers-sqlite is “baked into” the Airflow package, apache-airflow-providers-postgres is not and is an optional feature. Rather than listing apache-airflow[postgres] in your requirements.txt which would expose a cycle via the extra, one could either manually depend on apache-airflow and apache-airflow-providers-postgres separately as requirements. Bazel rules which need only apache-airflow can take it as a dependency, and rules which explicitly want to mix in apache-airflow-providers-postgres now can.
Alternatively, one could use rules_python's patching features to remove one leg of the dependency manually. For instance by making apache-airflow-providers-postgres not explicitly depend on apache-airflow or perhaps apache-airflow-providers-common-sql.
(bazel-downloader)=
The bzlmod pip.parse call supports pulling information from PyPI (or a compatible mirror) and it will ensure that the bazel downloader is used for downloading the wheels. This allows the users to use the credential helper to authenticate with the mirror and it also ensures that the distribution downloads are cached. It also avoids using pip altogether and results in much faster dependency fetching.
This can be enabled by experimental_index_url and related flags as shown in the {gh-path}examples/bzlmod/MODULE.bazel example.
When using this feature during the pip extension evaluation you will see the accessed indexes similar to below:
Loading: 0 packages loaded currently loading: docs/ Fetching module extension pip in @@//python/extensions:pip.bzl; starting Fetching https://pypi.org/simple/twine/
This does not mean that rules_python is fetching the wheels eagerly, but it rather means that it is calling the PyPI server to get the Simple API response to get the list of all available source and wheel distributions. Once it has got all of the available distributions, it will select the right ones depending on the sha256 values in your requirements_lock.txt file. The compatible distribution URLs will be then written to the MODULE.bazel.lock file. Currently users wishing to use the lock file with rules_python with this feature have to set an environment variable RULES_PYTHON_OS_ARCH_LOCK_FILE=0 which will become default in the next release.
Fetching the distribution information from the PyPI allows rules_python to know which whl should be used on which target platform and it will determine that by parsing the whl filename based on PEP600, PEP656 standards. This allows the user to configure the behaviour by using the following publicly available flags:
--@rules_python//python/config_settings:py_linux_libc for selecting the Linux libc variant.--@rules_python//python/config_settings:pip_whl for selecting whl distribution preference.--@rules_python//python/config_settings:pip_whl_osx_arch for selecting MacOS wheel preference.--@rules_python//python/config_settings:pip_whl_glibc_version for selecting the GLIBC version compatibility.--@rules_python//python/config_settings:pip_whl_muslc_version for selecting the musl version compatibility.--@rules_python//python/config_settings:pip_whl_osx_version for selecting MacOS version compatibility.(credential-helper)=
The “use Bazel downloader for python wheels” experimental feature includes support for the Bazel Credential Helper.
Your python artifact registry may provide a credential helper for you. Refer to your index's docs to see if one is provided.
See the Credential Helper Spec for details.
The simplest form of a credential helper is a bash script that accepts an arg and spits out JSON to stdout. For a service like Google Artifact Registry that uses ‘Basic’ HTTP Auth and does not provide a credential helper that conforms to the spec, the script might look like:
#!/bin/bash # cred_helper.sh ARG=$1 # but we don't do anything with it as it's always "get" # formatting is optional echo '{' echo ' "headers": {' echo ' "Authorization": ["Basic dGVzdDoxMjPCow=="]' echo ' }' echo '}'
Configure Bazel to use this credential helper for your python index example.com:
# .bazelrc build --credential_helper=example.com=/full/path/to/cred_helper.sh
Bazel will call this file like cred_helper.sh get and use the returned JSON to inject headers into whatever HTTP(S) request it performs against example.com.