Ray Framework

Ray is an open-source unified framework for scaling AI and Python applications from a single laptop to a massive cluster with minimal code changes.

Ray eliminates the complexity of distributed computing by providing a simple API for parallelizing Python workloads. It serves as the underlying compute engine for industry leaders like OpenAI (training GPT-4) and Uber (powering Michelangelo). The framework consists of three core layers: a low-latency distributed runtime, a set of ecosystem libraries for training (Ray Train), serving (Ray Serve), and tuning (Ray Tune), and a robust data processing engine (Ray Data). By automating orchestration and auto-scaling, Ray allows developers to focus on model logic rather than managing infrastructure nodes or complex networking protocols.

https://www.ray.io