![]() Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. ![]() With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is a unified way to scale Python and AI applications from a laptop to a cluster. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands. Today’s ML workloads are increasingly compute-intensive. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing Monitor and debug Ray applications and clusters using the Ray dashboard. Objects: Immutable values accessible across the cluster. ![]() Tasks: Stateless functions executed in the cluster.Īctors: Stateful worker processes created in the cluster. Or more about Ray Core and its key abstractions: Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute: Ray is a unified framework for scaling AI and Python applications.
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