# Setting up task governance for Ray
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Set up Task Governance on your cluster first, then complete the Ray-specific steps on this page. For the concepts and the console setup, see [Setup for SageMaker HyperPod task governance](sagemaker-hyperpod-eks-operate-console-ui-governance-setup.md).

## Namespaces that need a compute allocation
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Task Governance admits a Ray workload only in a namespace that has a compute allocation. Create an allocation for every namespace where you create Ray workloads. A workload in a namespace with no allocation stays pending and is never admitted. For the console steps, see [Policies](sagemaker-hyperpod-eks-operate-console-ui-governance-policies.md).

## Gang scheduling
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Confirm that gang scheduling is enabled for your cluster. A Ray cluster needs its head and all of its workers running together, so without gang scheduling a partially scheduled cluster holds capacity without making progress. Task Governance implements gang scheduling with the Kueue `waitForPodsReady` feature, which evicts and requeues a workload whose pods do not all become ready within the configured timeout. For the configuration settings, see [Using gang scheduling in Amazon SageMaker HyperPod task governance](sagemaker-hyperpod-eks-operate-console-ui-governance-tasks-gang-scheduling.md).

## Verify
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Create a small `RayCluster` in an allocated namespace and confirm it reaches a running state. If it stays pending, confirm the namespace has a compute allocation with room for the declared cluster size.

## 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).