Getting Involved / Contributing

Ray is more than a framework for distributed applications but also an active community of developers, researchers, and folks that love machine learning.


Ask questions on our forum! The community is extremely active in helping people succeed in building their Ray applications.

You can join (and Star!) us on on GitHub.

Contributing to Ray

We welcome (and encourage!) all forms of contributions to Ray, including and not limited to:

  • Code reviewing of patches and PRs.

  • Pushing patches.

  • Documentation and examples.

  • Community participation in forums and issues.

  • Code readability and code comments to improve readability.

  • Test cases to make the codebase more robust.

  • Tutorials, blog posts, talks that promote the project.

What can I work on?

We use Github to track issues, feature requests, and bugs. Take a look at the ones labeled “good first issue” and “help wanted” for a place to start.

Setting up your development environment

To edit the Ray source code, you’ll want to checkout the repository and also build Ray from source. Follow these instructions for building a local copy of Ray to easily make changes.

Submitting and Merging a Contribution

There are a couple steps to merge a contribution.

  1. First merge the most recent version of master into your development branch.

    git remote add upstream
    git pull upstream master
  2. Make sure all existing tests pass.

  3. If introducing a new feature or patching a bug, be sure to add new test cases in the relevant file in ray/python/ray/tests/.

  4. Document the code. Public functions need to be documented, and remember to provide an usage example if applicable.

  5. Request code reviews from other contributors and address their comments. One fast way to get reviews is to help review others’ code so that they return the favor. You should aim to improve the code as much as possible before the review. We highly value patches that can get in without extensive reviews.

  6. Reviewers will merge and approve the pull request; be sure to ping them if the pull request is getting stale.


Even though we have hooks to run unit tests automatically for each pull request, we recommend you to run unit tests locally beforehand to reduce reviewers’ burden and speedup review process.

pytest ray/python/ray/tests/

Documentation should be documented in Google style format.

Testing for Python development

Suppose that one of the tests in a file of tests, e.g., python/ray/tests/, is failing. You can run just that test file locally as follows:

python -m pytest -v python/ray/tests/

However, this will run all of the tests in the file, which can take some time. To run a specific test that is failing, you can do the following instead:

pytest -v -k [test substring]

When running tests, usually only the first test failure matters. A single test failure often triggers the failure of subsequent tests in the same file.

# Stop after first failure.
pytest -x

Testing for C++ development

To compile and run all C++ tests, you can run:

bazel test $(bazel query 'kind(cc_test, ...)')

Alternatively, you can also run one specific C++ test. You can use:

bazel test $(bazel query 'kind(cc_test, ...)') --test_filter=ClientConnectionTest --test_output=streamed

Lint and Formatting


Python 3.7 is recommended. You will run into flake8 issues with Python 3.8.

We also have tests for code formatting and linting that need to pass before merge. Install yapf==0.23, flake8, flake8-quotes.

  • yapf version 0.23.0 (pip install yapf==0.23.0)

  • flake8 version 3.7.7 (pip install flake8==3.7.7)

  • flake8-quotes (pip install flake8-quotes)

  • If developing for C++, you will need clang-format version 7.0.0 (download this version of Clang from here)


On MacOS X, don’t use HomeBrew to install clang-format, as the only version available is too new.

You can run the following locally:


An output like the following indicates failure:

WARNING: clang-format is not installed!  # This is harmless
 * branch                master     -> FETCH_HEAD
python/ray/util/sgd/tf/ F401 'numpy as np' imported but unused  # Below is the failure

In addition, there are other formatting checkers for components like the following:

  • Python README format:

cd python
python check --restructuredtext --strict --metadata
  • Bazel format:


Understanding CI test jobs

The Ray project automatically runs continuous integration (CI) tests once a PR is opened using Travis-CI with multiple CI test jobs.

The Travis CI test folder contains all integration test scripts and they invoke other test scripts via pytest, bazel-based test or other bash scripts. Some of the examples include:

  • Raylet integration tests commands:
    • bazel test //:core_worker_test

  • Bazel test command:
    • bazel test --build_tests_only //:all

  • Ray serving test commands:
    • pytest python/ray/serve/tests

    • python python/ray/serve/examples/

If a Travis-CI build exception doesn’t appear to be related to your change, please visit this link to check recent tests known to be flaky.

Becoming a Reviewer

We identify reviewers from active contributors. Reviewers are individuals who not only actively contribute to the project and are also willing to participate in the code review of new contributions. A pull request to the project has to be reviewed by at least one reviewer in order to be merged. There is currently no formal process, but active contributors to Ray will be solicited by current reviewers.

More Resources for Getting Involved

Ray is more than a framework for distributed applications but also an active community of developers, researchers, and folks that love machine learning. Here’s a list of tips for getting involved with the Ray community:


These tips are based off of the TVM contributor guide.