awesome-ray
github.com/jiahaoyao/awesome-ray ↗Ray - A curated list of resources: https://github.com/ray-project/ray
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me legacy / archived resources from awesome-ray"
Installation instructions →What's inside
Models and Projects
- AlpaLegacy / Archived
- Anyscale AI Infra CookbookReference Architectures and Cookbooks
A collection of runnable, Ray-orchestrated examples spanning the full LLM lifecycle: data curation, Megatron training, SkyRL post-training, vLLM/SGLang serving, robotics, and fault-tolerant MoE deployment.
- AutoGluonMisc
- Aviary / RayLLMLegacy / Archived
- Aws-samplesMisc
- BalsaRay + Database
Videos
- 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
Annual Ray conference with recorded sessions on YouTube (Nov 3-5, 2025, San Francisco)
Tutorials and Blog Posts
- 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
books
- Learning Ray
Flexible Distributed Python for Machine Learning
course
Papers
- 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
cheatsheet
Showing a sample of 85 resources. View the full list on GitHub →