Building from source#

First, obtain the JAX source code:

git clone
cd jax

Building JAX involves two steps:

  1. Building or installing jaxlib, the C++ support library for jax.

  2. Installing the jax Python package.

Building or installing jaxlib#

Installing jaxlib with pip#

If you’re only modifying Python portions of JAX, we recommend installing jaxlib from a prebuilt wheel using pip:

pip install jaxlib

See the JAX readme for full guidance on pip installation (e.g., for GPU and TPU support).

Building jaxlib from source#

To build jaxlib from source, you must also install some prerequisites:

  • a C++ compiler (g++, clang, or MSVC)

    On Ubuntu or Debian you can install the necessary prerequisites with:

    sudo apt install g++ python python3-dev

    If you are building on a Mac, make sure XCode and the XCode command line tools are installed.

    See below for Windows build instructions.

  • there is no need to install Python dependencies locally, as your system Python will be ignored during the build; please check Managing hermetic Python for details.

To build jaxlib for CPU or TPU, you can run:

python build/
pip install dist/*.whl  # installs jaxlib (includes XLA)

To build a wheel for a version of Python different from your current system installation pass --python_version flag to the build command:

python build/ --python_version=3.12

The rest of this document assumes that you are building for Python version matching your current system installation. If you need to build for a different version, simply append --python_version=<py version> flag every time you call python build/ Note, the Bazel build will always use a hermetic Python installation regardless of whether the --python_version parameter is passed or not.

There are two ways to build jaxlib with CUDA support: (1) use python build/ --enable_cuda to generate a jaxlib wheel with cuda support, or (2) use python build/ --enable_cuda --build_gpu_plugin --gpu_plugin_cuda_version=12 to generate three wheels (jaxlib without cuda, jax-cuda-plugin, and jax-cuda-pjrt). You can set gpu_plugin_cuda_version to 11 or 12.

See python build/ --help for configuration options, including ways to specify the paths to CUDA and CUDNN, which you must have installed. Here python should be the name of your Python 3 interpreter; on some systems, you may need to use python3 instead. Despite calling the script with python, Bazel will always use its own hermetic Python interpreter and dependencies, only the build/ script itself will be processed by your system Python interpreter. By default, the wheel is written to the dist/ subdirectory of the current directory.

Building jaxlib from source with a modified XLA repository.#

JAX depends on XLA, whose source code is in the XLA GitHub repository. By default JAX uses a pinned copy of the XLA repository, but we often want to use a locally-modified copy of XLA when working on JAX. There are two ways to do this:

  • use Bazel’s override_repository feature, which you can pass as a command line flag to as follows:

    python build/ --bazel_options=--override_repository=xla=/path/to/xla
  • modify the WORKSPACE file in the root of the JAX source tree to point to a different XLA tree.

To contribute changes back to XLA, send PRs to the XLA repository.

The version of XLA pinned by JAX is regularly updated, but is updated in particular before each jaxlib release.

Additional Notes for Building jaxlib from source on Windows#

On Windows, follow Install Visual Studio to set up a C++ toolchain. Visual Studio 2019 version 16.5 or newer is required. If you need to build with CUDA enabled, follow the CUDA Installation Guide to set up a CUDA environment.

JAX builds use symbolic links, which require that you activate Developer Mode.

You can either install Python using its Windows installer, or if you prefer, you can use Anaconda or Miniconda to set up a Python environment.

Some targets of Bazel use bash utilities to do scripting, so MSYS2 is needed. See Installing Bazel on Windows for more details. Install the following packages:

pacman -S patch coreutils

Once coreutils is installed, the realpath command should be present in your shell’s path.

Once everything is installed. Open PowerShell, and make sure MSYS2 is in the path of the current session. Ensure bazel, patch and realpath are accessible. Activate the conda environment. The following command builds with CUDA enabled, adjust it to whatever suitable for you:

python .\build\ `
  --enable_cuda `
  --cuda_path='C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.1' `
  --cudnn_path='C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.1' `
  --cuda_version='10.1' `

To build with debug information, add the flag --bazel_options='--copt=/Z7'.

Additional notes for building a ROCM jaxlib for AMD GPUs#

You need several ROCM/HIP libraries installed to build for ROCM. For example, on a Ubuntu machine with AMD’s apt repositories available, you need a number of packages installed:

sudo apt install miopen-hip hipfft-dev rocrand-dev hipsparse-dev hipsolver-dev \
    rccl-dev rccl hip-dev rocfft-dev roctracer-dev hipblas-dev rocm-device-libs

To build jaxlib with ROCM support, you can run the following build command, suitably adjusted for your paths and ROCM version.

python build/ --enable_rocm --rocm_path=/opt/rocm-5.7.0

AMD’s fork of the XLA repository may include fixes not present in the upstream XLA repository. If you experience problems with the upstream repository, you can try AMD’s fork, by cloning their repository:

git clone

and override the XLA repository with which JAX is built:

python build/ --enable_rocm --rocm_path=/opt/rocm-5.7.0 \

Managing hermetic Python#

To make sure that JAX’s build is reproducible, behaves uniformly across supported platforms (Linux, Windows, MacOS) and is properly isolated from specifics of a local system, we rely on hermetic Python (see rules_python) for all build and test commands executed via Bazel. This means that your system Python installation will be ignored during the build and Python interpreter itself as well as all the Python dependencies will be managed by bazel directly.

Specifying Python version#

When you run build/ tool, the version of hermetic Python is set automatically to match the version of the Python you used to run build/ script. To choose a specific version explicitly you may pass --python_version argument to the tool:

python build/ --python_version=3.12

Under the hood, the hermetic Python version is controlled by HERMETIC_PYTHON_VERSION environment variable, which is set automatically when you run build/ In case you run bazel directly you may need to set the variable explicitly in one of the following ways:

# Either add an entry to your `.bazelrc` file
build --repo_env=HERMETIC_PYTHON_VERSION=3.12

# OR pass it directly to your specific build command
bazel build <target> --repo_env=HERMETIC_PYTHON_VERSION=3.12

# OR set the environment variable globally in your shell:

You may run builds and tests against different versions of Python sequentially on the same machine by simply switching the value of --python_version between the runs. All the python-agnostic parts of the build cache from the previous build will be preserved and reused for the subsequent builds.

Specifying Python dependencies#

During bazel build all JAX’s Python dependencies are pinned to their specific versions. This is necessary to ensure reproducibility of the build. The pinned versions of the full transitive closure of JAX’s dependencies together with their corresponding hashes are specified in build/requirements_lock_<python version>.txt files ( e.g. build/requirements_lock_3_12.txt for Python 3.12).

To update the lock files, make sure build/ contains the desired direct dependencies list and then execute the following command (which will call pip-compile under the hood):

python build/ --requirements_update --python_version=3.12

Alternatively, if you need more control, you may run the bazel command directly (the two commands are equivalent):

bazel run //build:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12

where 3.12 is the Python version you wish to update.

Note, since it is still pip and pip-compile tools used under the hood, so most of the command line arguments and features supported by those tools will be acknowledged by the Bazel requirements updater command as well. For example, if you wish the updater to consider pre-release versions simply pass --pre argument to the bazel command:

bazel run //build:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12 -- --pre

Specifying dependencies on local wheels#

If you need to depend on a local .whl file, for example on your newly built jaxlib wheel, you may add a path to the wheel in build/ and re-run the requirements updater command for a selected version of Python. For example:

echo -e "\n$(realpath jaxlib-0.4.27.dev20240416-cp312-cp312-manylinux2014_x86_64.whl)" >> build/
python build/ --requirements_update --python_version=3.12

Specifying dependencies on nightly wheels#

To build and test against the very latest, potentially unstable, set of Python dependencies we provide a special version of the dependency updater command as follows:

python build/ --requirements_nightly_update --python_version=3.12

Or, if you run bazel directly (the two commands are equivalent):

bazel run //build:requirements_nightly.update --repo_env=HERMETIC_PYTHON_VERSION=3.12

The difference between this and the regular updater is that by default it would accept pre-release, dev and nightly packages, it will also search as an extra index url and will not put hashes in the resultant requirements lock file.

Building with pre-release Python version#

We support all of the current versions of Python out of the box, but if you need to build and test against a different version (for example the latest unstable version which hasn’t been released officially yet) please follow the instructions below.

  1. Make sure you have installed necessary linux packages needed to build Python interpreter itself and key packages (like numpy or scipy) from source. On a typical Debian system you may need to install the following packages:

sudo apt-get update
sudo apt-get build-dep python3 -y
sudo apt-get install pkg-config zlib1g-dev libssl-dev -y
# to  build scipy
sudo apt-get install libopenblas-dev -y
  1. Check your WORKSPACE file and make sure it has custom_python_interpreter() entry there, pointing to the version of Python you want to build.

  2. Run bazel build @python_dev//:python_dev to build Python interpreter. By default it will be built with GCC compiler. If you wish to build with clang, you need to set corresponding env variables to do so ( e.g. --repo_env=CC=/usr/lib/llvm-17/bin/clang --repo_env=CXX=/usr/lib/llvm-17/bin/clang++).

  3. Check the output of the previous command. At the very end of it you will find a code snippet for python_register_toolchains() entry with your newly built Python in it. Copy that code snippet in your WORKSPACE file either right after python_init_toolchains() entry (to add the new version of Python) or instead of it (to replace an existing version, like replacing 3.12 with custom built variant of 3.12). The code snippet is generated to match your actual setup, so it should work as is, but you can customize it if you choose so (for example to change location of Python’s .tgz file so it could be downloaded remotely instead of being on local machine).

  4. Make sure there is an entry for your Python’s version in requirements parameter for python_init_repositories() in your WORKSPACE file. For example for Python 3.13 it should have something like "3.13": "//build:requirements_lock_3_13.txt".

  5. For unstable versions of Python, optionally (but highly recommended) run bazel build //build:all_py_deps --repo_env=HERMETIC_PYTHON_VERSION="3.13", where 3.13 is the version of Python interpreter you built on step 3. This will make pip pull and build from sources (for packages which don’t have binaries published yet, for example numpy, scipy, matplotlib, zstandard) all of the JAX’s python dependencies. It is recommended to do this step first (i.e. independently of actual JAX build) for all unstable versions of Python to avoid conflict between building JAX itself and building of its Python dependencies. For example, we normally build JAX with clang but building matplotlib from sources with clang fails out of the box due to differences in LTO behavior ( Link Time Optimization, triggered by -flto flag) between GCC and clang, and matplotlib assumes GCC by default. If you build against a stable version of Python, or in general you do not expect any of your Python dependencies to be built from sources (i.e. binary distributions for the corresponding Python version already exist in the repository) this step is not needed.

  6. Congrats, you’ve built and configured your custom Python for JAX project! You may now execute your built/test commands as usual, just make sure HERMETIC_PYTHON_VERSION environment variable is set and points to your new version.

  7. Note, if you were building a pre-release version of Python, updating of requirements_lock_<python_version>.txt files with your newly built Python is likely to fail, because package repositories will not have matching binary packages. When there are no binary packages available pip-compile proceeds with building them from sources, which is likely to fail because it is more restrictive than doing the same thing during pip installation. The recommended way to update requirements lock file for unstable versions of Python is to update requirements for the latest stable version (e.g. 3.12) without hashes (therefore special //build:requirements_dev.update target) and then copy the results to the unstable Python’s lock file (e.g. 3.13):

bazel run //build:requirements_dev.update --repo_env=HERMETIC_PYTHON_VERSION="3.12"
cp build/requirements_lock_3_12.txt build/requirements_lock_3_13.txt
bazel build //build:all_py_deps --repo_env=HERMETIC_PYTHON_VERSION="3.13"
# You may need to edit manually the resultant lock file, depending on how ready
# your dependencies are for the new version of Python.

Installing jax#

Once jaxlib has been installed, you can install jax by running:

pip install -e .  # installs jax

To upgrade to the latest version from GitHub, just run git pull from the JAX repository root, and rebuild by running or upgrading jaxlib if necessary. You shouldn’t have to reinstall jax because pip install -e sets up symbolic links from site-packages into the repository.

Running the tests#

There are two supported mechanisms for running the JAX tests, either using Bazel or using pytest.

Using Bazel#

First, configure the JAX build by running:

python build/ --configure_only

You may pass additional options to to configure the build; see the jaxlib build documentation for details.

By default the Bazel build runs the JAX tests using jaxlib built from source. To run JAX tests, run:

bazel test //tests:cpu_tests //tests:backend_independent_tests

//tests:gpu_tests and //tests:tpu_tests are also available, if you have the necessary hardware.

To use a preinstalled jaxlib instead of building it you first need to make it available in the hermetic Python. To install a specific version of jaxlib within hermetic Python run (using jaxlib >= 0.4.26 as an example):

echo -e "\njaxlib >= 0.4.26" >> build/
python build/ --requirements_update

Alternatively, to install jaxlib from a local wheel (assuming Python 3.12):

echo -e "\n$(realpath jaxlib-0.4.26-cp312-cp312-manylinux2014_x86_64.whl)" >> build/
python build/ --requirements_update --python_version=3.12

Once you have jaxlib installed hermetically, run:

bazel test --//jax:build_jaxlib=false //tests:cpu_tests //tests:backend_independent_tests

A number of test behaviors can be controlled using environment variables (see below). Environment variables may be passed to JAX tests using the --test_env=FLAG=value flag to Bazel.

Some of JAX tests are for multiple accelerators (i.e. GPUs, TPUs). When JAX is already installed, you can run GPUs tests like this:

bazel test //tests:gpu_tests --local_test_jobs=4 --test_tag_filters=multiaccelerator --//jax:build_jaxlib=false --test_env=XLA_PYTHON_CLIENT_ALLOCATOR=platform

You can speed up single accelerator tests by running them in parallel on multiple accelerators. This also triggers multiple concurrent tests per accelerator. For GPUs, you can do it like this:

MULTI_GPU="--run_under $PWD/build/ --test_env=JAX_ACCELERATOR_COUNT=${NB_GPUS} --test_env=JAX_TESTS_PER_ACCELERATOR=${JOBS_PER_ACC} --local_test_jobs=$J"
bazel test //tests:gpu_tests //tests:backend_independent_tests --test_env=XLA_PYTHON_CLIENT_PREALLOCATE=false --test_tag_filters=-multiaccelerator $MULTI_GPU

Using pytest#

First, install the dependencies by running pip install -r build/test-requirements.txt.

To run all the JAX tests using pytest, we recommend using pytest-xdist, which can run tests in parallel. It is installed as a part of pip install -r build/test-requirements.txt command.

From the repository root directory run:

pytest -n auto tests

Controlling test behavior#

JAX generates test cases combinatorially, and you can control the number of cases that are generated and checked for each test (default is 10) using the JAX_NUM_GENERATED_CASES environment variable. The automated tests currently use 25 by default.

For example, one might write

# Bazel
bazel test //tests/... --test_env=JAX_NUM_GENERATED_CASES=25`


# pytest
JAX_NUM_GENERATED_CASES=25 pytest -n auto tests

The automated tests also run the tests with default 64-bit floats and ints (JAX_ENABLE_X64):

JAX_ENABLE_X64=1 JAX_NUM_GENERATED_CASES=25 pytest -n auto tests

You can run a more specific set of tests using pytest’s built-in selection mechanisms, or alternatively you can run a specific test file directly to see more detailed information about the cases being run:


You can skip a few tests known to be slow, by passing environment variable JAX_SKIP_SLOW_TESTS=1.

To specify a particular set of tests to run from a test file, you can pass a string or regular expression via the --test_targets flag. For example, you can run all the tests of jax.numpy.pad using:

python tests/ --test_targets="testPad"

The Colab notebooks are tested for errors as part of the documentation build.


JAX uses pytest in doctest mode to test the code examples within the documentation. You can run this using

pytest docs

Additionally, JAX runs pytest in doctest-modules mode to ensure code examples in function docstrings will run correctly. You can run this locally using, for example:

pytest --doctest-modules jax/_src/numpy/

Keep in mind that there are several files that are marked to be skipped when the doctest command is run on the full package; you can see the details in ci-build.yaml

Type checking#

We use mypy to check the type hints. To check types locally the same way as the CI checks them:

pip install mypy
mypy --config=pyproject.toml --show-error-codes jax

Alternatively, you can use the pre-commit framework to run this on all staged files in your git repository, automatically using the same mypy version as in the GitHub CI:

pre-commit run mypy


JAX uses the ruff linter to ensure code quality. You can check your local changes by running:

pip install ruff
ruff jax

Alternatively, you can use the pre-commit framework to run this on all staged files in your git repository, automatically using the same ruff version as the GitHub tests:

pre-commit run ruff

Update documentation#

To rebuild the documentation, install several packages:

pip install -r docs/requirements.txt

And then run:

sphinx-build -b html docs docs/build/html -j auto

This can take a long time because it executes many of the notebooks in the documentation source; if you’d prefer to build the docs without executing the notebooks, you can run:

sphinx-build -b html -D nb_execution_mode=off docs docs/build/html -j auto

You can then see the generated documentation in docs/build/html/index.html.

The -j auto option controls the parallelism of the build. You can use a number in place of auto to control how many CPU cores to use.

Update notebooks#

We use jupytext to maintain two synced copies of the notebooks in docs/notebooks: one in ipynb format, and one in md format. The advantage of the former is that it can be opened and executed directly in Colab; the advantage of the latter is that it makes it much easier to track diffs within version control.

Editing ipynb#

For making large changes that substantially modify code and outputs, it is easiest to edit the notebooks in Jupyter or in Colab. To edit notebooks in the Colab interface, open and Upload from your local repo. Update it as needed, Run all cells then Download ipynb. You may want to test that it executes properly, using sphinx-build as explained above.

Editing md#

For making smaller changes to the text content of the notebooks, it is easiest to edit the .md versions using a text editor.

Syncing notebooks#

After editing either the ipynb or md versions of the notebooks, you can sync the two versions using jupytext by running jupytext --sync on the updated notebooks; for example:

pip install jupytext==1.16.0
jupytext --sync docs/notebooks/thinking_in_jax.ipynb

The jupytext version should match that specified in .pre-commit-config.yaml.

To check that the markdown and ipynb files are properly synced, you may use the pre-commit framework to perform the same check used by the github CI:

git add docs -u  # pre-commit runs on files in git staging.
pre-commit run jupytext

Creating new notebooks#

If you are adding a new notebook to the documentation and would like to use the jupytext --sync command discussed here, you can set up your notebook for jupytext by using the following command:

jupytext --set-formats ipynb,md:myst path/to/the/notebook.ipynb

This works by adding a "jupytext" metadata field to the notebook file which specifies the desired formats, and which the jupytext --sync command recognizes when invoked.

Notebooks within the Sphinx build#

Some of the notebooks are built automatically as part of the pre-submit checks and as part of the Read the docs build. The build will fail if cells raise errors. If the errors are intentional, you can either catch them, or tag the cell with raises-exceptions metadata (example PR). You have to add this metadata by hand in the .ipynb file. It will be preserved when somebody else re-saves the notebook.

We exclude some notebooks from the build, e.g., because they contain long computations. See exclude_patterns in

Documentation building on

JAX’s auto-generated documentation is at

The documentation building is controlled for the entire project by the readthedocs JAX settings. The current settings trigger a documentation build as soon as code is pushed to the GitHub main branch. For each code version, the building process is driven by the .readthedocs.yml and the docs/ configuration files.

For each automated documentation build you can see the documentation build logs.

If you want to test the documentation generation on Readthedocs, you can push code to the test-docs branch. That branch is also built automatically, and you can see the generated documentation here. If the documentation build fails you may want to wipe the build environment for test-docs.

For a local test, I was able to do it in a fresh directory by replaying the commands I saw in the Readthedocs logs:

mkvirtualenv jax-docs  # A new virtualenv
mkdir jax-docs  # A new directory
cd jax-docs
git clone --no-single-branch --depth 50
cd jax
git checkout --force origin/test-docs
git clean -d -f -f
workon jax-docs

python -m pip install --upgrade --no-cache-dir pip
python -m pip install --upgrade --no-cache-dir -I Pygments==2.3.1 setuptools==41.0.1 docutils==0.14 mock==1.0.1 pillow==5.4.1 alabaster>=0.7,<0.8,!=0.7.5 commonmark==0.8.1 recommonmark==0.5.0 'sphinx<2' 'sphinx-rtd-theme<0.5' 'readthedocs-sphinx-ext<1.1'
python -m pip install --exists-action=w --no-cache-dir -r docs/requirements.txt
cd docs
python `which sphinx-build` -T -E -b html -d _build/doctrees-readthedocs -D language=en . _build/html