Learning Ray: Flexible Distributed Python for Machine Learning

Ray is an open-source distributed computing framework that simplifies scaling compute-intensive Python workloads. This book teaches Python programmers, data engineers, and data scientists how to leverage Ray locally and spin up compute clusters for machine learning applications. It covers building applications with Ray, hyperparameter optimization, reinforcement learning, distributed training, data processing, working with Ray Clusters, and serving models with Ray Serve.

Format: Paperback / softback
Length: 271 pages
Publication date: 03 March 2023
Publisher: O'Reilly Media

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 to work with Ray Clusters and serve models with Ray Serve, and build end-to-end machine learning applications with Ray AIR.

Ray is an open-source distributed computing framework designed to simplify the process of scaling compute-intensive Python workloads. It provides a high-level API that allows developers to easily distribute their tasks across multiple machines, enabling them to achieve faster processing speeds and better resource utilization. Ray is built on top of a number of other open-source projects, including Apache Spark, Apache Hadoop, and Python, and it leverages their strengths to provide a powerful and flexible platform for distributed computing.

One of the key features of Ray is its ability to scale horizontally, which means that it can easily add more machines to a cluster as needed to handle increasing workloads. This scalability is particularly useful for machine learning applications, which often require large amounts of data and computational power to train and evaluate models. Ray also provides a number of tools and libraries that are specifically designed for machine learning, such as Ray Tune, Ray RLlib, and Ray Datasets. These tools help developers optimize their algorithms, train their models more efficiently, and analyze their data more effectively.

In addition to its scalability and machine learning-specific features, Ray is also easy to use and deploy. It has a simple and intuitive API that makes it easy to write and run distributed applications, and it supports a wide range of programming languages, including Python, Java, and Scala. Ray also has a large and active community of developers who are constantly working to improve and expand the framework, making it a reliable and robust choice for any distributed computing project.

Overall, Ray is a powerful and flexible distributed computing framework that is well-suited for scaling compute-intensive Python workloads. Whether you are working on machine learning, data analysis, or any other type of application that requires distributed processing, Ray can help you achieve faster processing speeds and better resource utilization. With its scalability, machine learning-specific features, and easy-to-use API, Ray is a great choice for developers who want to build high-performance and scalable applications.

Weight: 488g
Dimension: 177 x 233 x 18 (mm)
ISBN-13: 9781098117221