Ray Explained: A Practical Guide to Its Core and AI Library Architecture

Ray is an open-source distributed compute layer for Python and AI workloads, offering low-level primitives alongside higher-level libraries for data processing, model training, hyperparameter tuning, and serving. The framework addresses key distributed computing challenges — such as scheduling, process management, and object movement across machines — without requiring developers to manage those details manually. Its three core building blocks are tasks (stateless remote functions), actors (stateful remote class instances), and object references (handles to values stored anywhere in the cluster). Rather than replacing infrastructure tools like Kubernetes, Ray focuses narrowly on compute orchestration, leaving application-level concerns such as user management and model lifecycle to the developer or platform above it. The article targets Ray version 2.55.1 and uses small, locally runnable examples to illustrate how these components fit together in a real machine learning workflow.