Learning Ray (Paperback)

Max Pumperla

ISBN 10: 1098117220 / ISBN 13: 9781098117221

Published by O'Reilly Media, Sebastopol, 2023

About this Item

Paperback. Get started with Ray, the open source distributed computing framework that 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 machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. Learn how to build your first distributed applications with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLlib library for reinforcement learning Manage distributed training with the Ray Train library Use Ray to perform data processing with Ray Datasets Learn how work with Ray Clusters and serve models with Ray Serve Build end-to-end machine learning applications with Ray AIR 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781098117221

Bibliographic Details

• Title: Learning Ray (Paperback)
• Publisher: O'Reilly Media, Sebastopol
• Publication Date: 2023
• Language: English
• ISBN 10: 1098117220
• ISBN 13: 9781098117221
• Binding: Paperback
• Condition: new

About this title

Synopsis

Get started with Ray, the open source distributed computing framework that 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 machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.

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

About the Authors

Max Pumperla is a data science professor and software engineer located in Hamburg, Germany. He’s an active open source contributor, maintainer of several Python packages, and author of machine learning books. He currently works as software engineer at Anyscale. As head of product research at Pathmind Inc. he was developing reinforcement learning solutions for industrial applications at scale using Ray RLlib, Serve and Tune.

Edward Oakes (ed.nmi.oakes@gmail.com), writing chapters 7 (data) & 9 (serving): "Edward is a software engineer and team lead at Anyscale, where he leads the development of Ray Serve and is one of the top open source contributors to Ray. Prior to Anyscale, he was a graduate student in the EECS department at UC Berkeley."

RIchard Liaw (rliaw@berkeley.edu), writing chapters 6 (training) & 8 (clusters): Richard Liaw is a software engineer at Anyscale, working on open source tools for distributed machine learning. He is on leave from the PhD program at the Computer Science Department at UC Berkeley, advised by Joseph Gonzalez, Ion Stoica, and Ken Goldberg.