Scaling Python with Ray

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they’ve been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.

In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness.

If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You’ll explore distributed processing (the pure Python implementation of serverless) and learn how to:

• Implement stateful applications with Ray actors
• Build workflow management in Ray
• Use Ray as a unified system for batch and stream processing
• Apply advanced data processing with Ray
• Build microservices with Ray
• Implement reliable Ray applications

Table of Contents

Chapter 1. What Is Ray, and Where Does It Fit?

Chapter 2. Getting Started with Ray (Locally)

Chapter 3. Remote Functions

Chapter 4. Remote Actors

Chapter 5. Ray Design Details

Chapter 6. Implementing Streaming Applications

Chapter 7. Implementing Microservices

Chapter 8. Ray Workflows

Chapter 9. Advanced Data with Ray

Chapter 10. How Ray Powers Machine Learning

Chapter 11. Using GPUs and Accelerators with Ray

Chapter 12. Ray in the Enterprise

Appendix A. Space Beaver Case Study:

Actors, Kubernetes, and More

Appendix B. Installing and Deploying Ray

Appendix C. Debugging with Ray

About the Authors

Holden Karau is a queer transgender Canadian, Apache Spark committer, Apache Software Foundation member, and an active open source contributor. As a software engineer, she’s worked on a variety of distributed computing, search, and classification problems at Apple, Google, IBM, Alpine, Databricks, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor of mathematics in computer science. Outside of software, she enjoys playing with fire, welding, riding scooters, eating poutine, and dancing.

Boris Lublinsky is a Chief Architect for IBM’s Discovery Accelerator Platform, where he specializes in Kubernetes, serverless, workflows, and complex systems design. Boris has over 30 years of experience in enterprise architecture and software development. He is the co-author of Applied SOA (Wiley), Professional Hadoop Solutions (Wiley), Serving Machine Learning Models (Oâ??Reilly), and Kubeflow for Machine Learning (Oâ??Reilly). He is also a contributor to several open-source projects. Boris is a frequent speaker at numerous industry conferences and co-founder of several Chicago user groups.