Scaling Python with Ray

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

Ray is primarily a Python tool for fast and simple distributed computing. Ray was created by the RISELab at the University of California, Berkeley. An earlier iteration of this lab created the initial software that eventually became Apache Spark. Researchers from the RISELab started the company Anyscale to continue developing and to offer products and services around Ray.

Note

You can also use Ray from Java. Like many Python applications, under the hood Ray uses a lot of C++ and some Fortran. Ray streaming also has some Java components.

The goal of Ray is to solve a wider variety of problems than its predecessors, supporting various scalable programing models that range from actors to machine learning (ML) to data parallelism. Its remote function and actor models make it a truly general-purpose development environment instead of big data only.

Ray automatically scales compute resources as needed, allowing you to focus on your code instead of managing servers. In addition to traditional horizontal scaling (e.g., adding more machines), Ray can schedule tasks to take advantage of different machine sizes and accelerators like graphics processing units (GPUs).

Since the introduction of Amazon Web Services (AWS) Lambda, interest in serverless computing has exploded. In this cloud computing model, the cloud provider allocates machine resources on demand, taking care of the servers on behalf of its customers. Ray provides a great foundation ...