Test CI

If you are building distributed applications

Ray Core provides a simple, universal API for building distributed applications.

Ray accomplishes this mission by:

1. Providing simple primitives for building and running distributed applications.
2. Enabling end users to parallelize single machine code, with little to zero code changes.
3. Including a large ecosystem of applications, libraries, and tools on top of the core Ray to enable complex applications.

If you are building machine learning solutions

On top of Ray Core are several libraries for solving problems in machine learning:

• Ray Data (beta): distributed data loading and compute
• Ray Train: distributed deep learning
• Ray Tune: scalable hyperparameter tuning
• Ray RLlib: industry-grade reinforcement learning

As well as libraries for taking ML and distributed apps to production:

• Ray Serve: scalable and programmable serving
• Ray Workflows (alpha): fast, durable application flows

There are also many community integrations with Ray, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. Check out the full list of Ray distributed libraries here.

If you are deploying Ray on your infrastructure

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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:

• Join our community Slack to discuss Ray!
• Star and follow us on GitHub.
• To post questions or feature requests, check out the Discussion Board.
• Follow us and spread the word on Twitter.
• Join our Meetup Group to connect with others in the community.
• Use the [ray] tag on StackOverflow to ask and answer questions about Ray usage.

If you're interested in contributing to Ray, visit our page on Getting Involved to read about the contribution process and see what you can work on!