# Managing Ray workloads
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Ray workloads on HyperPod are KubeRay custom resources. You manage them from the Amazon SageMaker Studio web interface or by applying manifests with `kubectl`. Both surfaces act on the same resources, so you can move between them on the same workload.

**Topics**
+ [Managing Ray workloads with Studio](sagemaker-hyperpod-ray-manage-studio.md)
+ [Managing Ray workloads with kubectl](sagemaker-hyperpod-ray-manage-kubectl.md)

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon SageMaker. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query sagemaker` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).