What is Ray?¶
Ray provides a simple, universal API for building distributed applications.
Ray accomplishes this mission by:
Providing simple primitives for building and running distributed applications.
Enabling end users to parallelize single machine code, with little to zero code changes.
Including a large ecosystem of applications, libraries, and tools on top of the core Ray to enable complex applications.
Ray Core provides the simple primitives for application building.
On top of Ray Core are several libraries for solving problems in machine learning:
Getting Started with Ray¶
Check out A Gentle Introduction to Ray to learn more about Ray and its ecosystem of libraries that enable things like distributed hyperparameter tuning, reinforcement learning, and distributed training.
Ray provides Python and Java API. And Ray uses Tasks (functions) and Actors (Classes) to allow you to parallelize your code.
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 on GitHub.
To post questions or feature requests, check out the Ray Github Discussions page!
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!
Here are some talks, papers, and press coverage involving Ray and its libraries. Please raise an issue if any of the below links are broken, or if you’d like to add your own talk!