Learning Ray: Flexible Distributed Python for Machine Learning
Pumperla, Max; Oakes, Edward; Liaw, Richard
Language: English
Published by O'Reilly Media, 2023
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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In English.
Seller Inventory # ria9781098117221_new
- Title
- Learning Ray: Flexible Distributed Python for Machine Learning
- Author
- Pumperla, Max; Oakes, Edward; Liaw, Richard
- Publisher
- O'Reilly Media
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1098117220
- ISBN 13
- 9781098117221
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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