Ray is a general purpose distributed computing framework. At a low level, Ray provides fault-tolerant primitives that support applications running across multiple processors. At a higher level, Ray supports scalable reinforcement learning, including the common problem of hyperparameter tuning.

In a previous episode, we explored the primitives of Ray as well as Anyscale, the business built around Ray and reinforcement learning. In today’s episode, Richard Liaw explores some of the libraries and applications that sit on top of Ray. 

RLlib gives APIs for reinforcement learning such as policy serving and multi-agent environments. Tune gives developers an easy way to do scalable hyperparameter tuning, which is necessary for exploring different types of deep learning configurations. In a future show, we will explore Tune in more detail.