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Welcome to the Scaling Computer Vision Workloads with Ray Workshop!
This 2-day workshop is intended to teach you what Ray, Ray Core, Ray AI Runtime are and how to use them to scale ML applications and utilize large compute clusters.
During this event we will cover an introduction to Ray, and you’ll learn how to scale a computer vision workloads with Ray!
| 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. |
| Scaling model training | Learn about scaling model training in computer vision with Ray. |
| Ray observability | Introducing the Ray State API and Ray Dashboard UI as tools for observing the Ray cluster and applications. |
| Optional: Batch inference with Ray Datasets | Bonus content for scaling batch inference using Ray Datasets. |
You can learn and get more involved with the Ray community of developers and researchers:
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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.