Learning Ray: Flexible Distributed Python for Machine Learning

Get started with Ray, the open source distributed computing framework that greatly simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.

Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build reinforcement learning applications that serve trained models with Ray. You'll understand how Ray fits into the current landscape of data science tools and discover how this programming language continues to integrate ever more tightly with these tools. Distributed computation is hard, but with Ray you'll find it easy to get started.

• Learn how to build your first distributed application with Ray Core
• Conduct hyperparameter optimization with Ray Tune
• Use the Ray RLib library for reinforcement learning
• Manage distributed training with the RaySGD library
• Use Ray to perform data processing
• Learn how work with Ray Clusters and serve models with Ray Serve
• Build an end-to-end machine learning application with Ray