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
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- Introduction to Ray Framework: Ray is an open-source distributed computing framework that simplifies scaling compute-intensive Python workloads, making it accessible for programmers, data engineers, and scientists.
What is Ray, and what are its main uses?
Ray is an open-source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads, making it accessible for programmers, data engineers, and data scientists to build, manage, and run distributed applications.
