Ray - A Framework for Scaling and Distributing Python and ML Applications

Explore Ray, an open-source distributed framework for scaling Python and machine learning applications, in this comprehensive talk by Jules Damji, Lead Developer Advocate at Anyscale. Dive into Ray's architecture, core concepts, and primitives like remote Tasks and Actors. Learn about Ray's native libraries including Ray Tune, Ray Train, Ray Serve, Ray Datasets, and RLlib. Watch a practical demonstration using XGBoost for classification to understand how to scale training, hyperparameter tuning, and inference from a single node to a cluster. Gain insights into distributed computing trends, Ray's ecosystem, and design patterns. Discover how to tackle challenges in hyperparameter tuning using techniques like Bayesian optimization and early stopping. Get hands-on with code examples and learn about worker processes in Ray. Understand the performance benefits of using Ray for various machine learning workloads and how it can be applied in production environments.