Learning Ray: Flexible Distributed Python for Machine Learning

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

開始使用Ray，這個開源的分散式計算框架，大大簡化了擴展計算密集型Python工作負載的過程。這本實用書籍將教導Python程式設計師、資料工程師和資料科學家如何在本地和計算集群中使用Ray。您將能夠使用Ray來結構化和運行大規模的機器學習程式。

作者Max Pumperla、Edward Oakes和Richard Liaw向您展示如何使用Ray建立強化學習應用程式，並使用Ray提供訓練模型。您將了解Ray如何融入當前的資料科學工具生態系統，並發現這個程式語言如何與這些工具更緊密地整合。分散式計算很困難，但使用Ray，您將發現入門變得容易。

本書內容包括：
- 學習如何使用Ray Core建立您的第一個分散式應用程式
- 使用Ray Tune進行超參數優化
- 使用Ray RLib庫進行強化學習
- 使用RaySGD庫進行分散式訓練
- 使用Ray進行資料處理
- 學習如何使用Ray Clusters並使用Ray Serve提供模型
- 使用Ray建立端到端的機器學習應用程式