Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It is now used in many production deployments.
I’ll explain the problems that Ray solves and useful features it provides, such as rapid distribution, scheduling, and execution of “tasks” and management of distributed stateful “serverless” computing. I’ll illustrate how it’s used in several ML libraries. You’ll learn when to use Ray and how to use it in your projects.