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Contributing

Interested in contributing to TensorFlow Probability? We appreciate all kinds of help!

Pull Requests

We gladly welcome pull requests.

Before making any changes, we recommend opening an issue (if it doesn't already exist) and discussing your proposed changes. This will let us give you advice on the proposed changes. If the changes are minor, then feel free to make them without discussion.

Want to contribute but not sure of what? Here are a few suggestions:

  1. Add a new example or tutorial. Located in examples/, these are a great way to familiarize yourself and others with TFP tools.

  2. Solve an existing issue. These range from low-level software bugs to higher-level design problems. Check out the label good first issue.

All submissions, including submissions by project members, require review. After a pull request is approved, we merge it. Note our merging process differs from GitHub in that we pull and submit the change into an internal version control system. This system automatically pushes a git commit to the GitHub repository (with credit to the original author) and closes the pull request.

Continuous Integration

We use GitHub Actions to do automated style checking and run unit-tests (discussed in more detail below). A build will be triggered when you open a pull request, or update the pull request by adding a commit, rebasing etc.

We test against TensorFlow nightly on Python 3.7. We shard our tests across several build jobs (identified by the SHARD environment variable). Lints are also done in a separate job.

All pull-requests will need to pass the automated lint and unit-tests before being merged. As the tests can take a bit of time, see the following sections on how to run the lint checks and unit-tests locally while you're developing your change.

Style

See the TensorFlow Probability style guide. Running pylint detects many (but certainly not all) style issues. TensorFlow Probability follows a custom pylint configuration.

Unit tests

All TFP code-paths must be unit-tested; see this unit-test guide for recommended test setup.

Unit tests ensure new features (a) work correctly and (b) guard against future breaking changes (thus lower maintenance costs).

Setup

bazel

We use bazel to manage building, packaging, and testing TFP. You'll need to install bazel before running our tests (we have recently added some experimental support for running some tests with pytest, but for a variety of reasons this will probably never work 100%). See instructions here on installing bazel.

virtualenv

We strongly recommend running unit tests in an active virtualenv. Doing so requires some extra bazel flags, so we created a wrapper script, which we suggest using. An example invocation (presumed to run from the root of the TFP repo:

Dependencies

To run the unit tests, you'll need several packages installed (again, we strongly recommend you work in a virtualenv). We include a script to do this for you, which also does some sanity checks on the environtment:

./testing/install_test_dependencies.sh

See the header comments in that script for more details.

Helper scripts

# Run all TFP tests.
./testing/run_tfp_test.sh //tensorflow_probability/...
# Run one TFP test.
./testing/run_tfp_test.sh //tensorflow_probability/python/distributions:joint_distribution_coroutine_test
# Lint a file (requires pylint installed, e.g. via pip install pylint).
./testing/run_tfp_lints.sh tensorflow_probability/python/distributions/joint_distribution_coroutine.py

See comments at the top of the script for more info.

For convenience, also consider sourcing the following script to alias tfp_test and tfp_lints to the above script:

source ./testing/define_testing_alias.sh
source ./testing/define_linting_alias.sh
# Run all TFP tests.
tfp_test //tensorflow_probability/...
# Run one TFP test.
tfp_test //tensorflow_probability/python/distributions:joint_distribution_coroutine_test
# Lint a file.
tfp_lints tensorflow_probability/python/distributions/joint_distribution_coroutine.py

Additional considerations

As of early 2020, tensorflow and tf-nightly include GPU support by default, which means if you have a GPU installed and run tests with the default tf-nightly pip package, tests will try to run using the GPU. To avoid this, the dependency install script installs tf-nightly-cpu by default. If you want to run tests on the GPU, you can use pass --enable_gpu flag to testing/install_test_dependencies.sh. In this case, you will also need to include the flag --jobs=1, since by default Bazel will run many tests in parallel, and each one will try to claim all the GPU memory:

tfp_test --jobs=1 //tensorflow_probability/...

Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one.

You generally only need to submit a CLA once, so if you've already submitted one (even if it was for a different project), you probably don't need to do it again.