Welcome to the Ray documentation¶
What can you do with Ray?¶
What is Ray?¶
Ray is an open-source project developed at UC Berkeley RISE Lab. As a general-purpose and universal distributed compute framework, you can flexibly run any compute-intensive Python workload — from distributed training or hyperparameter tuning to deep reinforcement learning and production model serving.
Ray Core provides a simple, universal API for building distributed applications.
Ray’s native libraries and tools enable you to run complex ML applications with Ray.
You can deploy these applications on any of the major cloud providers, including AWS, GCP, and Azure, or run them on your own servers.
Ray also has a growing ecosystem of community integrations, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. The following figure gives you an overview of the Ray ecosystem.
How to get 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:
Get involved and become part of the Ray community
💡 Open an issue: Help us improve Ray by submitting feature requests, bug-reports, or simply ask for help and get support via GitHub issues.
👩💻 Create a pull request: Found a typo in the documentation? Want to add a new feature? Submit a pull request to help us improve Ray.
🐦 Follow us on Twitter: Stay up to date with the latest news and updates on Ray.
⭐ Star and follow us on GitHub: Support Ray by following its development on GitHub and give us a boost by starring the project.
🤝🏿 Join our Meetup Group: Join one of our community events to learn more about Ray and get a chance to meet the team behind Ray.
🙌 Discuss on Stack Overflow:
[ray] tag on Stack Overflow to ask and answer questions about Ray usage.
If you’re interested in contributing to Ray, check out our contributing guide to read about the contribution process and see what you can work on.