ray

Why we included this project

Ray is the distributed compute layer that many ML teams quietly build on top of. It takes a script that runs on a single laptop and lets the same code run across a cluster, with a Python-native runtime that parallelizes tasks and actors. Around that runtime sit purpose-built libraries: Ray Train for multi-node model training, Ray Tune for parallel hyperparameter search, Ray Serve for online model serving, and RLlib for distributed reinforcement learning. So one framework can carry a project from data preprocessing through training and tuning to deployment, which is exactly what a small team wants when it lacks dedicated infrastructure engineers. Data scientists and ML engineers who are hitting the ceiling of a single machine will find the most value, since Ray handles scheduling, fault tolerance, and autoscaling behind a familiar Python API. It is a substantial system to learn, but teams that standardize on one compute substrate for their ML workloads tend to find it worth the effort.