ray

Ray is an AI compute engine designed to scale Python and AI applications. It consists of a core distributed runtime and a suite of specialized AI libraries to simplify machine learning compute workloads. Key components include: Ray Core Abstractions: - Tasks: Stateless functions executed across the cluster. - Actors: Stateful worker processes. - Objects: Immutable values accessible throughout the cluster. Ray AI Libraries: - Data: For scalable ML datasets. - Train: For distributed training. - Tune: For scalable hyperparameter tuning. - RLlib: For scalable reinforcement learning. - Serve: For scalable and programmable model serving. Ray includes built-in observability tools such as the Ray Dashboard for monitoring and the Ray Distributed Debugger for troubleshooting. It is compatible with various environments, including local machines, cloud providers, and Kubernetes.