Ray is an open-source distributed computing framework for scaling Python and AI workloads from a laptop to large clusters. It provides a general execution layer for parallel and distributed tasks together with higher-level libraries for model training, hyperparameter tuning, reinforcement learning, data processing, and serving machine-learning applications. Developers can often scale existing Python code without manually building low-level distributed systems.

Ray is widely used in AI infrastructure because modern training, inference, and agent workloads frequently need to coordinate many CPUs, GPUs, processes, or machines. Components such as Ray Data, Ray Train, Ray Tune, RLlib, and Ray Serve cover different stages of the machine-learning lifecycle. The framework is best suited to engineering and research teams whose workloads have outgrown a single machine but who want a unified Python-native platform for distributed execution.