Ray: A System for High-performance, Distributed ML Applications
summary
Ray is a lightweight distributed runtime that scales Python from laptop to clusters (users run up to 10,000 nodes), exposes a tiny API — ray.remote for tasks and actors for state with ray.get to retrieve results — and powers RLlib for reinforcement learning. Examples show rapid scheduling of fine‑grained tasks, integration with PyTorch/TensorFlow, performance comparable to hand‑rolled implementations, and usable drop‑in replacements for multiprocessing and asyncio.
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chapters (14)
- 0:00Start
- 0:17Ray - Scalability from a Laptop to a Clust
- 1:44DOMINO
- 5:09The Ray Vision: Sharing a common Framework
- 5:42API - Designed to Be Intuitive and Concise
- 11:33Ray Libraries
- 15:33Go as a Reinforcement Learning Problem
- 17:12Diverse compute Requirements Motivated Creation of Rad
- 19:24What Are Microservices?
- 19:46Separate Management
- 20:33Management - Simplified
- 21:50If you're already using...
- 22:19Ray Community and Resources
- 23:00Conclusion
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