Ray: Distributed Computing For All, Part 2

This content is the second part of a series on the Ray library, focusing on deploying Python workloads across multi-server clusters in the cloud. It demonstrates how to scale a Python application from a local machine to a distributed environment with minimal code changes, specifically by modifying the ray.init() call. The guide details the prerequisites for setting up a Ray cluster on AWS and provides an in-depth walkthrough of configuring the cluster using a YAML file. It explains critical configuration sections for defining the provider, authentication, node types, and setup commands to prepare the cluster environment.