Название: Learning Ray: Flexible Distributed Python for Machine Learning (7th Early Release) Автор: Max Pumperla, Edward Oakes, Richard Liaw Издательство: O’Reilly Media, Inc. Год: 2022 Страниц: 220 Язык: английский Формат: epub (true), mobi Размер: 10.3 MB
Get started with Ray, the open source distributed computing framework that greatly 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 reinforcement learning applications that serve trained models with Ray. You'll understand how Ray fits into the current landscape of data science tools and discover how this programming language continues to integrate ever more tightly with these tools. Distributed computation is hard, but with Ray you'll find it easy to get started.
Distributed computing is a fascinating topic. Looking back at the early days of computing, I can’t help but be impressed by the fact that so many companies distribute their workloads across clusters of computers. It’s not only impressive because we have figured out efficient ways to do so, but it’s also becoming a necessity. Individual computers keep getting faster and more powerful, and yet our need for large scale computing keeps exceeding what single machines can do.
Ray simplifies distributed computing for non-experts and makes it easy to take Python scripts and scale them across multiple nodes. Ray is great at scaling both data and compute heavy workloads, such as data transformations and model training, and targets machine learning (ML) workloads with the need to scale. The addition of the Ray AI Runtime (AIR) with the release of Ray 2.0 in August 2022 increased the support for complex ML workloads in Ray even further.
Learn how to build your first distributed application with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLib library for reinforcement learning Manage distributed training with the RaySGD library Use Ray to perform data processing Learn how work with Ray Clusters and serve models with Ray Serve Build an end-to-end machine learning application with Ray
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