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Learning Ray: Flexible Distributed Python for Machine Learning de Max Pumperla

Learning Ray: Flexible Distributed Python for Machine Learning

Autor Max Pumperla, Edward Oakes, Richard Liaw
en Limba Engleză Paperback – 2 mar 2023
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.
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Specificații

ISBN-13: 9781098117221
ISBN-10: 1098117220
Pagini: 250
Dimensiuni: 181 x 232 x 15 mm
Greutate: 0.45 kg
Editura: O'Reilly

Descriere

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

Notă biografică

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.