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ResourceSpec

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Source code in docs_src/capo_sagemaker/types/resource_spec.py 
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class ResourceSpec(TypedDict, closed=True):
    sage_maker_image_arn: NotRequired["capo_sagemaker.types.image_arn.ImageArn"]
    """<p>The ARN of the SageMaker AI image that the image version belongs to.</p>"""
    sage_maker_image_version_arn: NotRequired[
        "capo_sagemaker.types.image_version_arn.ImageVersionArn"
    ]
    """<p>The ARN of the image version created on the instance. To clear the value set for <code>SageMakerImageVersionArn</code>, pass <code>None</code> as the value.</p>"""
    sage_maker_image_version_alias: NotRequired[
        "capo_sagemaker.types.image_version_alias.ImageVersionAlias"
    ]
    """<p>The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.</p>"""
    instance_type: NotRequired["capo_sagemaker.types.app_instance_type.AppInstanceType"]
    """<p>The instance type that the image version runs on.</p> <note> <p> <b>JupyterServer apps</b> only support the <code>system</code> value.</p> <p>For <b>KernelGateway apps</b>, the <code>system</code> value is translated to <code>ml.t3.medium</code>. KernelGateway apps also support all other values for available instance types.</p> </note>"""
    lifecycle_config_arn: NotRequired[
        "capo_sagemaker.types.studio_lifecycle_config_arn.StudioLifecycleConfigArn"
    ]
    """<p> The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.</p>"""
    training_plan_arn: NotRequired[
        "capo_sagemaker.types.studio_resource_spec_training_plan_arn.StudioResourceSpecTrainingPlanArn"
    ]
    """<p>The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.</p> <p>For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/training-plan-utilization-for-studio-apps.html">Using training plans in Studio applications</a>.</p>"""

instance_type instance-attribute 

instance_type: NotRequired[AppInstanceType]

The instance type that the image version runs on.

JupyterServer apps only support the system value.

For KernelGateway apps, the system value is translated to ml.t3.medium. KernelGateway apps also support all other values for available instance types.

lifecycle_config_arn instance-attribute 

lifecycle_config_arn: NotRequired[StudioLifecycleConfigArn]

The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

sage_maker_image_arn instance-attribute 

sage_maker_image_arn: NotRequired[ImageArn]

The ARN of the SageMaker AI image that the image version belongs to.

sage_maker_image_version_alias instance-attribute 

sage_maker_image_version_alias: NotRequired[ImageVersionAlias]

The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

sage_maker_image_version_arn instance-attribute 

sage_maker_image_version_arn: NotRequired[ImageVersionArn]

The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.

training_plan_arn instance-attribute 

training_plan_arn: NotRequired[StudioResourceSpecTrainingPlanArn]

The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.

For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.