Building from source#

First, obtain the JAX source code:

git clone https://github.com/google/jax
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.

  • Python packages: numpy, wheel.

You can install the necessary Python dependencies using pip:

pip install numpy wheel

To build jaxlib without CUDA GPU or TPU support (CPU only), you can run:

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

To build jaxlib with CUDA support, use python build/build.py --enable_cuda; to build with TPU support, use python build/build.py --enable_tpu.

See python build/build.py --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. By default, the wheel is written to the dist/ subdirectory of the current directory.

Building jaxlib from source with a modified TensorFlow repository.#

JAX depends on XLA, whose source code is in the Tensorflow GitHub repository. By default JAX uses a pinned copy of the TensorFlow 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 build.py as follows:

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

To contribute changes back to XLA, send PRs to the TensorFlow 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\build.py `
  --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' `
  --cudnn_version='7.6.5'

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

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 build.py 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/build.py --configure_only

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

By default the Bazel build runs the JAX tests using jaxlib built form 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 jaxlib from source, 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 --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:

NB_GPUS=2
JOBS_PER_ACC=4
J=$((NB_GPUS * JOBS_PER_ACC))
MULTI_GPU="--run_under $PWD/build/parallel_accelerator_execute.sh --test_env=JAX_ACCELERATOR_COUNT=${NB_GPUS} --test_env=JAX_TESTS_PER_ACCELERATOR=${JOBS_PER_ACC} --jobs=$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#

To run all the JAX tests using pytest, we recommend using pytest-xdist, which can run tests in parallel. First, install pytest-xdist and pytest-benchmark by running pip install -r build/test-requirements.txt. Then, 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`

or

# 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:

JAX_NUM_GENERATED_CASES=5 python tests/lax_numpy_test.py

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/lax_numpy_test.py --test_targets="testPad"

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

Doctests#

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/lax_numpy.py

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=mypy.ini --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

Linting#

JAX uses the flake8 linter to ensure code quality. You can check your local changes by running:

pip install flake8
flake8 jax

Alternatively, you can use the pre-commit framework to run this on all staged files in your git repository, automatically using the same flake8 version as the GitHub tests:

pre-commit run flake8

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 http://colab.research.google.com 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==1.13.8
jupytext --sync docs/notebooks/quickstart.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 conf.py.

Documentation building on readthedocs.io#

JAX’s auto-generated documentation is at https://jax.readthedocs.io/.

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/conf.py 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 https://github.com/google/jax
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