Название: Learning Ray: Flexible Distributed Python for Data Science (Early Release)
Автор: Max Pumperla, Edward Oakes, Richard Liaw
Издательство: O’Reilly Media, Inc.
Год: 2022-01-21
Язык: английский
Формат: pdf, epub
Размер: 10.2 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.

What I like about Ray is that it checks all the above boxes. It’s a flexible distributed computing framework build for the Python data science community. Ray is easy to get started and keeps simple things simple. Its core API is as lean as it gets and helps you reason effectively about the distributed programs you want to write. You can efficiently parallelize Python programs on your laptop, and run the code you tested locally on a cluster practically without any changes. Its high-level libraries are easy to configure and can seamlessly be used together. Some of them, like Ray’s reinforcement learning library, would have a bright future as standalone projects, distributed or not. While Ray’s core is built in C++, it’s been a Python-first framework since day one, integrates with many important data science tools, and can count on a growing ecosystem.

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