awesome-ray

Ray - A curated list of resources: https://github.com/ray-project/ray

GitHub Stars

Curated Resources

Use this list with your AI agent

Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:

"Show me ray + jax / tpu resources from awesome-ray"

AlpaRay + JAX / TPU
Auto parallelization for large-scale neural networks using Jax, XLA, and Ray
AutoGluonMisc
AviaryRay + LLM
evaluate multiple LLMs easily
Aws-samplesMisc
BalsaRay + Database
Daftdistributed computing

Anyscale AcademyAnyscale Academy & Official Tutorials
Ray tutorials from Anyscale with accompanying videos on YouTube
Anyscale YouTube ChannelAnyscale Academy & Official Tutorials
Official YouTube channel with Ray tutorials, conference talks, and educational content
Deep reinforcement learning at Riot Games by Ben KasperRLlib
reinforcement learning for game development in production
Ray Crash CourseAnyscale Academy & Official Tutorials
Introductory online class with video on Anyscale YouTube
Ray Summit 2024Conference Talks
Annual Ray conference with recorded sessions on YouTube (Sep 30 - Oct 2, 2024)
Ray Summit 2025Conference Talks
Upcoming conference (Nov 3-5, 2025, San Francisco)

Hacker News DiscussionEarlier Resources
Community discussion about Ray
How Ray Helps Power ChatGPT2024-2025
The New Stack - How OpenAI uses Ray for ChatGPT training coordination
Load PyTorch Models 340 Times Faster with RayEarlier Resources
IBM
Programming in Ray: Tips for first-time usersEarlier Resources
Berkeley RISE Lab
RAY: A Powerful Distributed Computing Framework for ML/AI2024-2025
Spheron Network (June 2024) - Covers Ray's capabilities for scaling models and distributed computing
Ray Summit 2024: Breaking Through the AI Complexity Wall2024-2025
Anyscale (2024) - Highlights from Ray Summit 2024, orchestrating 1M+ clusters per month

Ray: A Distributed Framework for Emerging AI Applications (OSDI 2018)Foundational Papers
The foundational paper presenting Ray's unified interface for task-parallel and actor-based computations. Demonstrates scaling beyond 1.8 million tasks per second.
Ray on arXivFoundational Papers
arXiv version of the foundational Ray paper

Showing a sample of 73 resources. View the full list on GitHub →