Learning Ray

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

Max is a data science professor and software engineer located in Hamburg, Germany. He’s an active open source contributor, maintainer of several Python packages, author of machine learning books and speaker at international conferences. As head of product research at Pathmind Inc. he’s developing reinforcement learning solutions for industrial applications at scale using Ray. P...

(展开全部)