What is Ray?

Ray is an open-source framework for running Python work across processes and machines. Teams use it for distributed training, hyperparameter tuning, data processing, and model serving.

KubeRay brings Ray to Kubernetes. Its operator manages resources such as RayJob and RayService, creates the Ray head and worker pods, and follows the Ray workload lifecycle.

How TauGrid uses Ray

TauGrid turns a repository target into either a Kubernetes Job or a KubeRay RayJob. A Ray workload follows this path:

1. tau run validates the target and creates a RayJob.
2. Kueue waits for the required quota.
3. Kubernetes places the Ray head and worker pods.
4. Ray coordinates the application across those pods.
5. TauGrid reports status, streams driver logs, and retrieves saved outputs.

Ray owns application execution and communication between workers. TauGrid owns the submission and run experience around it.

When Ray is useful

Use Ray when a workload needs multiple workers, Ray Train, Ray Tune, or Ray Serve. A simple single-container task can run as a standard Kubernetes Job.

The Ray dashboard shows live cluster and task state while the Ray head is running. Stellar serves a different purpose: it shows saved experiment metrics and comparisons after the workload produces evidence.

Try GPU Ray Tune HPO for a complete example.