Batch / Client / update_service_environment

update_service_environment

Batch.Client.update_service_environment(**kwargs)

Updates a service environment. You can update the state of a service environment from ENABLED to DISABLED to prevent new service jobs from being placed in the service environment.

See also: AWS API Documentation

Request Syntax

response = client.update_service_environment(
    serviceEnvironment='string',
    state='ENABLED'|'DISABLED',
    capacityLimits=[
        {
            'maxCapacity': 123,
            'capacityUnit': 'string'
        },
    ]
)
Parameters:

  • serviceEnvironment (string) –

    [REQUIRED]

    The name or ARN of the service environment to update.

  • state (string) – The state of the service environment.

  • capacityLimits (list) –

    The capacity limits for the service environment. This defines the maximum resources that can be used by service jobs in this environment.

    • (dict) –

      Defines the type and maximum quantity of resources that can be allocated to service jobs in a service environment.

      • maxCapacity (integer) –

        The maximum capacity available for the service environment. For a quota management enabled service environment, this value represents the maximum quantity of a particular resource type (specified by capacityUnit) that can be allocated to service jobs. For other service environments, this value represents the maximum quantity of all resources that can be allocated to service jobs.

        For example, if maxCapacity=50 and capacityUnit=NUM_INSTANCES, you can run up to 50 instances concurrently. If you run 5 SageMaker Training jobs that each use 10 instances, a subsequent job requiring 10 instances waits in the queue until capacity is available. In a quota management enabled service environment with capacityUnit=ml.m5.large, only ml.m5.large instances count against this limit, and jobs requiring other instance types wait until a matching capacity limit is configured.

      • capacityUnit (string) –

        The unit of measure for the capacity limit, which defines how maxCapacity is interpreted. For SAGEMAKER_TRAINING jobs in a quota management enabled service environment, specify the instance type (for example, ml.m5.large). Otherwise, use NUM_INSTANCES.

Return type:

dict

Returns:

Response Syntax

{
    'serviceEnvironmentName': 'string',
    'serviceEnvironmentArn': 'string'
}

Response Structure

  • (dict) –

    • serviceEnvironmentName (string) –

      The name of the service environment that was updated.

    • serviceEnvironmentArn (string) –

      The Amazon Resource Name (ARN) of the service environment that was updated.

Exceptions