Studying RAY. Flexible distributed computing in Python for machine learning
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This book will help Python programmers, engineers, and data researchers learn how to apply the open-source distributed computing framework Ray and deploy Ray computational clusters. Ray can be used for structuring and executing large-scale machine learning programs. Distributed computing can be complex, but with Ray, you can easily get started.
By reading the book, you will learn how to:
- create your first distributed applications using the core of the framework - Ray Core,
- optimize hyperparameters using the Ray Tune library,
- apply the Ray RLlib library for reinforcement learning,
- manage distributed model training using the Ray Train library,
- use Ray for data processing with the Ray Data library,
- work with Ray clusters and serve models as services using the Ray Serve library,
- create end-to-end machine learning applications using the Ray AIR toolkit.
By reading the book, you will learn how to:
- create your first distributed applications using the core of the framework - Ray Core,
- optimize hyperparameters using the Ray Tune library,
- apply the Ray RLlib library for reinforcement learning,
- manage distributed model training using the Ray Train library,
- use Ray for data processing with the Ray Data library,
- work with Ray clusters and serve models as services using the Ray Serve library,
- create end-to-end machine learning applications using the Ray AIR toolkit.
Author:
Author:Pumperla Max
Cover:
Cover:hardcover
Category:
- Category:Computer & Technology
- Category:Science & Math
Dimensions:
Dimensions:24x17x2 cm
ISBN:
ISBN:978-6-01083-430-9
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