© 2022, Anyscale Inc. All Rights Reserved
Welcome to a collection of education materials focused on Ray, a distributed compute framework for scaling your Python and machine learning workloads from a laptop to a cluster.
| Module | Description |
|---|---|
| Overview of Ray | An Overview of Ray and entire Ray ecosystem. |
| Introduction to Ray AI Runtime | An Overview of the Ray AI Runtime. |
| Ray Core: Remote Functions as Tasks | Learn how arbitrary functions to be executed asynchronously on separate Python workers. |
| Ray Core: Remote Objects | Learn about objects that can be stored anywhere in a Ray cluster. |
| Ray Core: Remote Classes as Actors, part 1 | Work with stateful actors. |
| Ray Core: Remote Classes as Actors, part 2 | Learn "Tree of Actors" pattern. |
| Scaling batch inference | Learn about scaling batch inference in computer vision with Ray. |
| Optional: Batch inference with Ray Datasets | Bonus content for scaling batch inference using Ray Datasets. |
| Scaling model training | Learn about scaling model training in computer vision with Ray. |
| Ray observability part 1 | Introducing the Ray State API and Ray Dashboard UI as tools for observing the Ray cluster and applications. |
You can learn and get more involved with the Ray community of developers and researchers:
-
Official Ray site
Browse the ecosystem and use this site as a hub to get the information that you need to get going and building with Ray. -
Join the community on Slack
Find friends to discuss your new learnings in our Slack space. -
Use the discussion board
Ask questions, follow topics, and view announcements on this community forum. -
Join a meetup group
Tune in on meet-ups to listen to compelling talks, get to know other users, and meet the team behind Ray. -
Open an issue
Ray is constantly evolving to improve developer experience. Submit feature requests, bug-reports, and get help via GitHub issues. -
Become a Ray contributor
We welcome community contributions to improve our documentation and Ray framework.