Ray - A System for High-performance, Distributed Python Applications

Explore Ray, an open-source distributed framework from U.C. Berkeley's RISELab, designed to scale Python applications from laptops to clusters with a focus on ML/AI system performance challenges. Learn about Ray's problem-solving capabilities, key features like rapid distribution, task scheduling and execution, and management of distributed stateful "serverless" computing. Discover how Ray is utilized in various ML libraries, when to implement it, and how to integrate it into your projects. This 26-minute PyCon US talk, presented by Dean Wampler, offers valuable insights into Ray's production deployments and its potential to enhance your Python applications' scalability and performance.