create_cluster_scheduler_config ¶

def create_cluster_scheduler_config(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    name: Optional["capo_sagemaker.types.entity_name.EntityName"] = None,
    cluster_arn: Optional["capo_sagemaker.types.cluster_arn.ClusterArn"] = None,
    scheduler_config: Optional[
        "capo_sagemaker.types.scheduler_config.SchedulerConfig"
    ] = None,
    description: Optional[
        "capo_sagemaker.types.entity_description.EntityDescription"
    ] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
) -> "capo_sagemaker.types.create_cluster_scheduler_config_response.CreateClusterSchedulerConfigResponse":
    """<p>Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.</p>

    Args:
        name: <p>The name for the cluster policy. The name must be unique within the SageMaker AI HyperPod cluster specified by <code>ClusterArn</code>. You can use the same name in other clusters within a Region or across Regions.</p>
        cluster_arn: <p>ARN of the cluster.</p>
        scheduler_config: <p>Configuration about the monitoring schedule.</p>
        description: <p>Description of the cluster policy.</p>
        tags: <p>Tags of the cluster policy.</p>

    Raises:
        capo_sagemaker.errors.conflict_exception.ConflictException: <p>There was a conflict when you attempted to modify a SageMaker entity such as an <code>Experiment</code> or <code>Artifact</code>.</p>
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.create_cluster_scheduler_config_request.CreateClusterSchedulerConfigRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_cluster_scheduler_config_response.CreateClusterSchedulerConfigResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.create_cluster_scheduler_config

        output, http_response = (
            capo_sagemaker._operations.sage_maker.create_cluster_scheduler_config.create_cluster_scheduler_config(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateClusterSchedulerConfig")
    )
    input_: capo_sagemaker.types.create_cluster_scheduler_config_request.CreateClusterSchedulerConfigRequest = {}
    if name is not None:
        input_["name"] = name
    if cluster_arn is not None:
        input_["cluster_arn"] = cluster_arn
    if scheduler_config is not None:
        input_["scheduler_config"] = scheduler_config
    if description is not None:
        input_["description"] = description
    if tags is not None:
        input_["tags"] = tags

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output