(building-from-source)= # Building from source First, obtain the JAX source code: ``` git clone https://github.com/jax-ml/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](https://github.com/jax-ml/jax#installation) for full guidance on pip installation (e.g., for GPU and TPU support). ### Building `jaxlib` from source ```{warning} While it should typically be possible to compile `jaxlib` from source using most modern compilers, the builds are only tested using clang. Pull requests are welcomed to improve support for different toolchains, but other compilers are not actively supported. ``` To build `jaxlib` from source, you must also install some prerequisites: - A C++ compiler: As mentioned in the box above, it is best to use a recent version of clang (at the time of writing, the version we test is 18), but other compilers (e.g. g++ or MSVC) may work. On Ubuntu or Debian you can follow the instructions from the [LLVM](https://apt.llvm.org/) documentation to install the latest stable version of clang. 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: for running the build helper script. Note that there is no need to install Python dependencies locally, as your system Python will be ignored during the build; please check [Managing hermetic Python](#managing-hermetic-python) for details. To build `jaxlib` for CPU or TPU, you can run: ``` python build/build.py build --wheels=jaxlib --verbose 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/build.py build --wheels=jaxlib --python_version=3.12 --verbose ``` 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=` flag every time you call `python build/build.py`. Note, the Bazel build will always use a hermetic Python installation regardless of whether the `--python_version` parameter is passed or not. If you would like to build `jaxlib` and the CUDA plugins: Run ``` python build/build.py build --wheels=jaxlib,jax-cuda-plugin,jax-cuda-pjrt ``` to generate three wheels (jaxlib without cuda, jax-cuda-plugin, and jax-cuda-pjrt). By default all CUDA compilation steps performed by NVCC and clang, but it can be restricted to clang via the `--build_cuda_with_clang` flag. See `python build/build.py --help` for configuration options. 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/build.py` script itself will be processed by your system Python interpreter. By default, the wheel is written to the `dist/` subdirectory of the current directory. * JAX versions starting from v.0.4.32: you can provide custom CUDA and CUDNN versions in the configuration options. Bazel will download them and use as target dependencies. To download the specific versions of CUDA/CUDNN redistributions, you can use the `--cuda_version` and `--cudnn_version` flags: ```bash python build/build.py build --wheels=jax-cuda-plugin --cuda_version=12.3.2 \ --cudnn_version=9.1.1 ``` or ```bash python build/build.py build --wheels=jax-cuda-pjrt --cuda_version=12.3.2 \ --cudnn_version=9.1.1 ``` Please note that these parameters are optional: by default Bazel will download CUDA and CUDNN redistribution versions provided in `.bazelrc` in the environment variables `HERMETIC_CUDA_VERSION` and `HERMETIC_CUDNN_VERSION` respectively. To point to CUDA/CUDNN/NCCL redistributions on local file system, you can use the following command: ```bash python build/build.py build --wheels=jax-cuda-plugin \ --bazel_options=--repo_env=LOCAL_CUDA_PATH="/foo/bar/nvidia/cuda" \ --bazel_options=--repo_env=LOCAL_CUDNN_PATH="/foo/bar/nvidia/cudnn" \ --bazel_options=--repo_env=LOCAL_NCCL_PATH="/foo/bar/nvidia/nccl" ``` Please see the full list of instructions in [XLA documentation](https://github.com/openxla/xla/blob/main/docs/hermetic_cuda.md). * JAX versions prior v.0.4.32: you must have CUDA and CUDNN installed and provide paths to them using configuration options. ### Building jaxlib from source with a modified XLA repository. JAX depends on XLA, whose source code is in the [XLA GitHub repository](https://github.com/openxla/xla). 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 `build.py` as follows: ``` python build/build.py build --wheels=jaxlib --local_xla_path=/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 Note: JAX does not support CUDA on Windows; use WSL2 for CUDA support. On Windows, follow [Install Visual Studio](https://docs.microsoft.com/en-us/visualstudio/install/install-visual-studio?view=vs-2019) to set up a C++ toolchain. Visual Studio 2019 version 16.5 or newer is required. JAX builds use symbolic links, which require that you activate [Developer Mode](https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development). You can either install Python using its [Windows installer](https://www.python.org/downloads/), or if you prefer, you can use [Anaconda](https://docs.anaconda.com/anaconda/install/windows/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html#windows-installers) to set up a Python environment. Some targets of Bazel use bash utilities to do scripting, so [MSYS2](https://www.msys2.org) is needed. See [Installing Bazel on Windows](https://bazel.build/install/windows#install-compilers) 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. ``` python .\build\build.py build --wheels=jaxlib ``` 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](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html), 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 ``` The recommended way to install these dependencies is by running our script, `jax/build/rocm/tools/get_rocm.py`, and selecting the appropriate options. To build jaxlib with ROCM support, you can run the following build commands, suitably adjusted for your paths and ROCM version. ``` python3 ./build/build.py build --wheels=jaxlib,jax-rocm-plugin,jax-rocm-pjrt --rocm_version=60 --rocm_path=/opt/rocm-6.2.3 ``` to generate three wheels (jaxlib without rocm, jax-rocm-plugin, and jax-rocm-pjrt) 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 https://github.com/ROCm/xla.git ``` and override the XLA repository with which JAX is built: ``` python3 ./build/build.py build --wheels=jax-rocm-plugin --rocm_version=60 --rocm_path=/opt/rocm-6.2.3 --local_xla_path=/rel/xla/ ``` For a simplified installation process, we also recommend checking out the `jax/build/rocm/dev_build_rocm.py script`. ## 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 (provided by [rules_python](https://github.com/bazelbuild/rules_python), see [Toolchain Registration](https://rules-python.readthedocs.io/en/latest/toolchains.html#workspace-toolchain-registration) for details) 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/build.py` tool, the version of hermetic Python is set automatically to match the version of the Python you used to run `build/build.py` script. To choose a specific version explicitly you may pass `--python_version` argument to the tool: ``` python build/build.py 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/build.py`. 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 --repo_env=HERMETIC_PYTHON_VERSION=3.12 # OR set the environment variable globally in your shell: export HERMETIC_PYTHON_VERSION=3.12 ``` 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_.txt` files ( e.g. `build/requirements_lock_3_12.txt` for `Python 3.12`). To update the lock files, make sure `build/requirements.in` contains the desired direct dependencies list and then execute the following command (which will call [pip-compile](https://pypi.org/project/pip-tools/) under the hood): ``` python build/build.py 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 By default the build scans `dist` directory in the repository root for any local `.whl` files to be included in the list of dependencies. If the wheel is Python version specific, only the wheels that match the selected Python version will be included. The overall local wheel search and selection logic is controlled by the arguments to `python_init_repositories()` macro (called directly from the `WORKSPACE` file). You may use `local_wheel_dist_folder` to change the location of the folder with local wheels. Use `local_wheel_inclusion_list` and `local_wheel_exclusion_list` arguments to specify which wheels should be included and/or excluded from the search (it supports basic wildcard matching). If necessary, you can also depend on a local `.whl` file manually, bypassing the automatic local wheel search mechanism. For example to depend on your newly built jaxlib wheel, you may add a path to the wheel in `build/requirements.in` 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/requirements.in python build/build.py 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/build.py requirements_update --python_version=3.12 --nightly_update ``` 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 https://pypi.anaconda.org/scientific-python-nightly-wheels/simple as an extra index url and will not put hashes in the resultant requirements lock file. ### Customizing hermetic Python (Advanced Usage) We support all of the current versions of Python out of the box, so unless your workflow has very special requirements (such as ability to use your own custom Python interpreter) you may safely skip this section entirely. In short, if you rely on a non-standard Python workflow you still can achieve the great level of flexibility in hermetic Python setup. Conceptually there will be only one difference compared to non-hermetic case: you will need to think in terms of files, not installations (i.e. think what files your build actually depends on, not what files need to be installed on your system), the rest is pretty much the same. So, in practice, to gain full control over your Python environment, hermetic or not you need to be able to do the following three things: 1) Specify which python interpreter to use (i.e. pick actual `python` or `python3` binary and libs that come with it in the same folder). 2) Specify a list of Python dependencies (e.g. `numpy`) and their actual versions. 3) Be able to add/remove/update dependencies in the list easily. Each dependency itself could be custom too (self-built for example). You already know how to do all of the steps above in a non-hermetic Python environment, here is how you do the same in the hermetic one (by approaching it in terms of files, not installations): 1) Instead of installing Python, get Python interpreter in a `tar` or `zip` file. Depending on your case you may simply pull one of many existing ones (such as [python-build-standalone](https://github.com/indygreg/python-build-standalone/releases)), or build your own and pack it in an archive (following official [build instructions](https://devguide.python.org/getting-started/setup-building/#compile-and-build) will do just fine). E.g. on Linux it will look something like the following: ``` ./configure --prefix python make -j12 make altinstall tar -czpf my_python.tgz python ``` Once you have the tarball ready, plug it in the build by pointing `HERMETIC_PYTHON_URL` env var to the archive (either local one or from the internet): ``` --repo_env=HERMETIC_PYTHON_URL="file:///local/path/to/my_python.tgz" --repo_env=HERMETIC_PYTHON_SHA256= # OR --repo_env=HERMETIC_PYTHON_URL="https://remote/url/to/my_python.tgz" --repo_env=HERMETIC_PYTHON_SHA256= # We assume that top-level folder in the tarbal is called "python", if it is # something different just pass additional HERMETIC_PYTHON_PREFIX parameter --repo_env=HERMETIC_PYTHON_URL="https://remote/url/to/my_python.tgz" --repo_env=HERMETIC_PYTHON_SHA256= --repo_env=HERMETIC_PYTHON_PREFIX="my_python/install" ``` 2) Instead of doing `pip install` create `requirements_lock.txt` file with full transitive closure of your dependencies. You may also depend on the existing ones already checked in this repo (as long as they work with your custom Python version). There are no special instructions on how you do it, you may follow steps recommended in [Specifying Python dependencies](#specifying-python-dependencies) from this doc, just call pip-compile directly (note, the lock file must be hermetic, but you can always generate it from non-hermetic python if you'd like) or even create it manually (note, hashes are optional in lock files). 3) If you need to update or customize your dependencies list, you may once again follow the [Specifying Python dependencies](#specifying-python-dependencies) instructions to update `requirements_lock.txt`, call pip-compile directly or modify it manually. If you have a custom package you want to use just point to its `.whl` file directly (remember, work in terms of files, not installations) from your lock (note, `requirements.txt` and `requirements_lock.txt` files support local wheel references). If your `requirements_lock.txt` is already specified as a dependency to `python_init_repositories()` in `WORKSPACE` file you don't have to do anything else. Otherwise you can point to your custom file as follows: ``` --repo_env=HERMETIC_REQUIREMENTS_LOCK="/absolute/path/to/custom_requirements_lock.txt" ``` Also note if you use `HERMETIC_REQUIREMENTS_LOCK` then it fully controls list of your dependencies and the automatic local wheels resolution logic described in [Specifying dependencies on local wheels](#specifying-dependencies-on-local-wheels) gets disabled to not interfere with it. That is it. To summarize: if you have an archive with Python interpreter in it and a requirements_lock.txt file with full transitive closure of your dependencies then you fully control your Python environment. #### Custom hermetic Python examples Note, for all of the examples below you may also set the environment variables globally (i.e. `export` in your shell instead of `--repo_env` argument to your command) so calling bazel via `build/build.py` will work just fine. Build with custom `Python 3.13` from the internet, using default `requirements_lock_3_13.txt` already checked in this repo (i.e. custom interpreter but default dependencies): ``` bazel build --repo_env=HERMETIC_PYTHON_VERSION=3.13 --repo_env=HERMETIC_PYTHON_URL="https://github.com/indygreg/python-build-standalone/releases/download/20241016/cpython-3.13.0+20241016-x86_64-unknown-linux-gnu-install_only.tar.gz" --repo_env=HERMETIC_PYTHON_SHA256="2c8cb15c6a2caadaa98af51df6fe78a8155b8471cb3dd7b9836038e0d3657fb4" ``` Build with custom Python 3.13 from local file system and custom lock file (assuming the lock file was put in `jax/build` folder of this repo before running the command): ``` bazel test --repo_env=HERMETIC_PYTHON_VERSION=3.13 --repo_env=HERMETIC_PYTHON_URL="file:///path/to/cpython.tar.gz" --repo_env=HERMETIC_PYTHON_PREFIX="prefix/to/strip/in/cython/tar/gz/archive" --repo_env=HERMETIC_PYTHON_SHA256= --repo_env=HERMETIC_REQUIREMENTS_LOCK="/absolute/path/to/build:custom_requirements_lock.txt" ``` If default python interpreter is good enough for you and you just need a custom set of dependencies: ``` bazel test --repo_env=HERMETIC_PYTHON_VERSION=3.13 --repo_env=HERMETIC_REQUIREMENTS_LOCK="/absolute/path/to/build:custom_requirements_lock.txt" ``` Note, you can have multiple different `requirement_lock.txt` files corresponding to the same Python version to support different scenarios. You can control which one is selected by specifying `HERMETIC_PYTHON_VERSION`. For example in `WORKSPACE` file: ``` requirements = { "3.10": "//build:requirements_lock_3_10.txt", "3.11": "//build:requirements_lock_3_11.txt", "3.12": "//build:requirements_lock_3_12.txt", "3.13": "//build:requirements_lock_3_13.txt", "3.13-scenario1": "//build:scenario1_requirements_lock_3_13.txt", "3.13-scenario2": "//build:scenario2_requirements_lock_3_13.txt", }, ``` Then you can build and test different combinations of stuff without changing anything in your environment: ``` # To build with scenario1 dependendencies: bazel test --repo_env=HERMETIC_PYTHON_VERSION=3.13-scenario1 # To build with scenario2 dependendencies: bazel test --repo_env=HERMETIC_PYTHON_VERSION=3.13-scenario2 # To build with default dependendencies: bazel test --repo_env=HERMETIC_PYTHON_VERSION=3.13 # To build with scenario1 dependendencies and custom Python 3.13 interpreter: bazel test --repo_env=HERMETIC_PYTHON_VERSION=3.13-scenario1 --repo_env=HERMETIC_PYTHON_URL="file:///path/to/cpython.tar.gz" --repo_env=HERMETIC_PYTHON_SHA256= ``` ## 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-tests)= ## 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 using the `--configure_only` flag. Pass `--wheel_list=jaxlib` for CPU tests and CUDA/ROCM for GPU for GPU tests: ``` python build/build.py build --wheels=jaxlib --configure_only python build/build.py build --wheels=jax-cuda-plugin --configure_only python build/build.py build --wheels=jax-rocm-plugin --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 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/requirements.in python build/build.py 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/requirements.in python build/build.py 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: ``` 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=${JOBS_PER_ACC} --local_test_jobs=$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` ``` 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](https://docs.pytest.org/en/latest/usage.html#specifying-tests-selecting-tests)'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. ### Hypothesis tests Some of the tests use [hypothesis](https://hypothesis.readthedocs.io/en/latest). Normally, hypothesis will test using multiple example inputs, and on a test failure it will try to find a smaller example that still results in failure: Look through the test failure for a line like the one below, and add the decorator mentioned in the message: ``` You can reproduce this example by temporarily adding @reproduce_failure('6.97.4', b'AXicY2DAAAAAEwAB') as a decorator on your test case ``` For interactive development, you can set the environment variable `JAX_HYPOTHESIS_PROFILE=interactive` (or the equivalent flag `--jax_hypothesis_profile=interactive`) in order to set the number of examples to 1, and skip the example minimization phase. ### Doctests JAX uses pytest in doctest mode to test the code examples within the documentation. You can find the up-to-date command to run doctests in [`ci-build.yaml`](https://github.com/jax-ml/jax/blob/main/.github/workflows/ci-build.yaml). E.g., you can run: ``` JAX_TRACEBACK_FILTERING=off XLA_FLAGS=--xla_force_host_platform_device_count=8 pytest -n auto --tb=short --doctest-glob='*.md' --doctest-glob='*.rst' docs --doctest-continue-on-failure --ignore=docs/multi_process.md --ignore=docs/jax.experimental.array_api.rst ``` 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: ``` JAX_TRACEBACK_FILTERING=off XLA_FLAGS=--xla_force_host_platform_device_count=8 pytest --doctest-modules jax/_src/numpy/lax_numpy.py ``` ## Type checking We use `mypy` to check the type hints. To run `mypy` with the same configuration as the github CI checks, you can use the [pre-commit](https://pre-commit.com/) framework: ``` pip install pre-commit pre-commit run mypy --all-files ``` Because `mypy` can be somewhat slow when checking all files, it may be convenient to only check files you have modified. To do this, first stage the changes (i.e. `git add` the changed files) and then run this before committing the changes: ``` pre-commit run mypy ``` ## Linting JAX uses the [ruff](https://docs.astral.sh/ruff/) linter to ensure code quality. To run `ruff` with the same configuration as the github CI checks, you can use the [pre-commit](https://pre-commit.com/) framework: ``` pip install pre-commit pre-commit run ruff --all-files ``` ## 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)= ### Update notebooks We use [jupytext](https://jupytext.readthedocs.io/) 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](https://jupytext.readthedocs.io/) by running `jupytext --sync` on the updated notebooks; for example: ``` pip install jupytext==1.16.4 jupytext --sync docs/notebooks/thinking_in_jax.ipynb ``` The jupytext version should match that specified in [.pre-commit-config.yaml](https://github.com/jax-ml/jax/blob/main/.pre-commit-config.yaml). To check that the markdown and ipynb files are properly synced, you may use the [pre-commit](https://pre-commit.com/) framework to perform the same check used by the github CI: ``` pip install pre-commit pre-commit run jupytext --all-files ``` #### 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](https://jax.readthedocs.io/en/latest) 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](https://github.com/jax-ml/jax/pull/2402/files)). 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](https://github.com/jax-ml/jax/blob/main/docs/conf.py). ### Documentation building on `readthedocs.io` JAX's auto-generated documentation is at . The documentation building is controlled for the entire project by the [readthedocs JAX settings](https://readthedocs.org/dashboard/jax). 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](https://readthedocs.org/projects/jax/builds/). 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](https://jax.readthedocs.io/en/test-docs/). If the documentation build fails you may want to [wipe the build environment for test-docs](https://docs.readthedocs.io/en/stable/guides/wipe-environment.html). 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/jax-ml/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 ```