从Ray开始，这是一个开源分布式计算框架，它极大地简化了扩展计算密集型Python工作负载的过程。通过这本实用的书，Python程序员、数据工程师和数据科学家将学习如何在本地利用Ray并加速计算集群。你将能够使用Ray大规模地构建和运行机器学习程序。
作者Max Pumperla、Edward Oakes和Richard Liaw向您展示了如何构建强化学习应用程序，为Ray的训练模型提供服务。您将了解Ray如何融入当前的数据科学工具领域，并了解这种编程语言如何继续与这些工具紧密集成。分布式计算很难，但是有了Ray，你会发现很容易开始。
了解如何使用Ray Core构建第一个分布式应用程序
使用光线调谐进行超参数优化
使用Ray RLib库进行强化学习
使用RaySGD图书馆管理分布式培训
使用光线执行数据处理
了解如何使用光线群集，以及如何使用光线服务为模型提供服务
使用Ray构建端到端机器学习应用程序
Learning Ray (Third Early Release)
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.
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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