Interested in contributing to SynapseML? We're excited to work with you.

You can contribute in many ways:

• Use the library and give feedback: report bugs, request features.
• Add sample Jupyter notebooks, Python or Scala code examples, documentation pages.
• Fix bugs and issues.
• Add new features, such as data transformations or machine learning algorithms.
• Review pull requests from other contributors.

How to contribute?

You can give feedback, report bugs and request new features anytime by opening an issue. Also, you can up-vote or comment on existing issues.

If you want to add code, examples or documentation to the repository, follow this process:

Propose a contribution

• Preferably, get started by tackling existing issues to get yourself acquainted with the library source and the process.
• To ensure your contribution is a good fit and doesn't duplicate on-going work, open an issue or comment on an existing issue. In it, discuss your contribution and design.
• Any algorithm you're planning to contribute should be well known and accepted for production use, and backed by research papers.
• Algorithms should be highly scalable and suitable for massive datasets.
• All contributions need to comply with the MIT License. Contributors external to Microsoft need to sign CLA.

Implement your contribution

• Fork the SynapseML repository.
• Implement your algorithm in Scala, using our wrapper generation mechanism to produce PySpark bindings.
• Use SparkML PipelineStages so your algorithm can be used as a part of pipeline.
• For parameters use MMLParams.
• Implement model saving and loading by extending SparkML MLReadable.
• Use good Scala style.
• Binary dependencies should be on Maven Central.
• See this pull request for an example contribution.

Implement tests

• Set up build environment. Use a Linux machine or VM (we use Ubuntu, but other distros should work too).
• Test your code locally.
• Add tests using ScalaTests. Unit tests are required.
• A sample notebook is required as an end-to-end test.

Implement documentation

• Add a sample Jupyter notebook that shows the intended use case of your algorithm, with instructions in step-by-step manner. (The same notebook could be used for testing the code.)
• Add in-line ScalaDoc comments to your source code, to generate the API reference documentation

Open a pull request

• In most cases, you should squash your commits into one.
• Open a pull request, and link it to the discussion issue you created earlier.
• A SynapseML core team member will trigger a build to test your changes.
• Fix any build failures. (The pull request will have comments from the build with useful links.)
• Wait for code reviews from core team members and others.
• Fix issues found in code review and reiterate.

Build and check-in

• Wait for a core team member to merge your code in.
• Your feature will be available through a Docker image and script installation in the next release, which typically happens around once a month. You can try out your features sooner by using build artifacts for the version that has your changes merged in (such versions end with a .devN).

If in doubt about how to do something, see how it was done in existing code or pull requests, and don't hesitate to ask.