Ray is an open source platform that supports model training, testing, and deployment, developed by RISELab at the University of California, Berkeley. It aims to simplify the development and deployment of large-scale machine learning, reinforcement learning, and distributed computing tasks. Ray is designed to provide high performance, flexibility, and ease of use, allowing developers to easily build and scale complex distributed applications. Whether it is processing massive data, training deep learning models, or running reinforcement learning algorithms, Ray can provide strong support.