First, obtain the JAX source code:

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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 installingjaxlib from a prebuilt wheel using pip:

See the JAX readme for fullguidance on pip installation (e.g., for GPU 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:

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

  See below for Windows build instructions.

• Python packages: numpy, scipy, six, wheel.

  The six package is required for during the jaxlib build only, and is notrequired at install time.

You can install the necessary Python dependencies using pip:

To build jaxlib with CUDA support, you can run:

See pythonbuild/build.py--help for configuration options, including ways tospecify the paths to CUDA and CUDNN, which you must have installed. Herepython should be the name of your Python 3 interpreter; on some systems, youmay need to use python3 instead. By default, the wheel is written to thedist/ subdirectory of the current directory.

To build jaxlib without CUDA GPU support (CPU only), drop the --enable_cuda:

Additional Notes for Building jaxlib from source on Windows¶

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

You can either install Python using itsWindows installer, or if you prefer, youcan use Anacondaor Minicondato setup a Python environment.

Some targets of Bazel use bash utilities to do scripting, so MSYS2is needed. See Installing Bazel on Windowsfor more details. Install the following packages:

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

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:

To upgrade to the latest version from GitHub, just run gitpull from the JAXrepository root, and rebuild by running build.py or upgrading jaxlib ifnecessary. You shouldn’t have to reinstall jax because pipinstall-esets up symbolic links from site-packages into the repository.

To run all the JAX tests, we recommend using pytest-xdist, which can run tests inparallel. First, install pytest-xdist and pytest-benchmark by runningpipinstallpytest-xdistpytest-benchmark.Then, from the repository root directory run:

JAX generates test cases combinatorially, and you can control the number ofcases that are generated and checked for each test (default is 10). The automated testscurrently use 25:

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

You can run a more specific set of tests usingpytest’sbuilt-in selection mechanisms, or alternatively you can run a specific testfile directly to see more detailed information about the cases being run:

You can skip a few tests known as slow, by passing environment variableJAX_SKIP_SLOW_TESTS=1.

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

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

Note that to run the full pmap tests on a (multi-core) CPU only machine, youcan run:

I.e. don’t use the -n auto option, since that effectively runs each test on asingle-core worker.

We use mypy to check the type hints. To check types locally the same wayas Travis checks them:

To rebuild the documentation, install several packages:

You must also install pandoc in order to regenerate the notebooks.See Install Pandoc,or using Miniconda whichI have used successfully on the Mac: condainstall-cconda-forgepandoc.If you do not want to install pandoc then you should regenerate the documentationwithout the notebooks.

You run at top-level one of the following commands:

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

Update notebooks¶

Open the notebook with http://colab.research.google.com (then Upload from yourlocal repo), update it as needed, Runallcells thenDownloadipynb. You may want to test that it executes properly, using sphinx-build asexplained above.

Some of the notebooks are built automatically as part of the Travis pre-submit checks andas 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 elsere-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 documentations is at jax.readthedocs.io.

The documentation building is controlled for the entire project by thereadthedocs JAX settings. The current settingstrigger a documentation build as soon as code is pushed to the GitHub master 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 thedocumentation build logs.

If you want to test the documentation generation on Readthedocs, you can push code to the test-docsbranch. That branch is also built automatically, and you cansee the generated documentation here.

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

Training and Teaching

The members of the MathJax team are professors in their own institutions with a long and successful track record in teaching and research. We regularly give presentations and workshops on the use of MathJax and its accessibility features for online teaching at international events.

Support for Online Teaching

MathJax is compatible with most Learning Management systems. We can help you to transfer your mathematical teaching materials to the web, allowing your faculty to teach mathematics online in an inclusive and accessible manner.

Support for Online Examinations

Remote online examinations are increasingly important. MathJax can help in preparing exam materials that are not only visually of the highest quality but also ensures that they are accessible for all students regardless of their individual needs.

Staff Training

We train teachers, faculty, and staff on how to prepare fully accessible math course materials. Training programs can be tailored to your specific requirements and those of your audience. We cover a variety of topics including:

• porting math documents from sources like LaTeX, Word, and PDF to web formats containing SVG and MathJax,
• generating mathematical material that is both web-ready and ePub compatible,
• web accessibility and WCAG guidelines for teaching material in mathematics, and
• an introduction to assistive technologies for STEM subjects.

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Please contact us for more information on how to get your teaching online and the training programs we can provide.