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Welcome to the Scaling NLP Workloads with Ray and Anyscale tutorial!
This tutorial is intended to teach you how you can use Ray AI Runtime and Anyscale to scale NLP applications and utilize large compute clusters.
You'll work on the text generation use cases (NLP). You'll fine-tune Google's FLAN-T5 model on the recent Alpaca dataset. You will also run distributed hyperparameter tuning and batch inference.
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.
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Join the community on Slack Find friends to discuss your new learnings in our Slack space.
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Use the discussion board Ask questions, follow topics, and view announcements on this community forum.
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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.
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Open an issue Ray is constantly evolving to improve developer experience. Submit feature requests, bug-reports, and get help via GitHub issues.
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Become a Ray contributor We welcome community contributions to improve our documentation and Ray framework.
You can further explore Ray ecosystem and expand your distributed ML skills by checking supplementary resources.
| 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. |
| Scaling CV batch inference | Use SegFormer architecture to learn about scaling batch inference in computer vision with Ray. |
| Scaling CV model training | Use SegFormer architecture to learn about scaling model training in computer vision with Ray. |
