TrainingPlanSummary ¶

class TrainingPlanSummary(TypedDict, closed=True):
    training_plan_arn: NotRequired[
        "capo_sagemaker.types.training_plan_arn.TrainingPlanArn"
    ]
    """<p>The Amazon Resource Name (ARN); of the training plan.</p>"""
    training_plan_name: NotRequired[
        "capo_sagemaker.types.training_plan_name.TrainingPlanName"
    ]
    """<p>The name of the training plan.</p>"""
    status: NotRequired["capo_sagemaker.types.training_plan_status.TrainingPlanStatus"]
    """<p>The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the <code>Status</code> attribute within the <code> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingPlanSummary.html">TrainingPlanSummary</a> </code> object.</p>"""
    status_message: NotRequired[
        "capo_sagemaker.types.training_plan_status_message.TrainingPlanStatusMessage"
    ]
    """<p>A message providing additional information about the current status of the training plan.</p>"""
    duration_hours: NotRequired[
        "capo_sagemaker.types.training_plan_duration_hours.TrainingPlanDurationHours"
    ]
    """<p>The number of whole hours in the total duration for this training plan.</p>"""
    duration_minutes: NotRequired[
        "capo_sagemaker.types.training_plan_duration_minutes.TrainingPlanDurationMinutes"
    ]
    """<p>The additional minutes beyond whole hours in the total duration for this training plan.</p>"""
    start_time: NotRequired["capo_sagemaker.types.timestamp.Timestamp"]
    """<p>The start time of the training plan.</p>"""
    end_time: NotRequired["capo_sagemaker.types.timestamp.Timestamp"]
    """<p>The end time of the training plan.</p>"""
    upfront_fee: NotRequired["capo_sagemaker.types.string256.String256"]
    """<p>The upfront fee for the training plan.</p>"""
    currency_code: NotRequired["capo_sagemaker.types.currency_code.CurrencyCode"]
    """<p>The currency code for the upfront fee (e.g., USD).</p>"""
    total_instance_count: NotRequired[
        "capo_sagemaker.types.total_instance_count.TotalInstanceCount"
    ]
    """<p>The total number of instances reserved in this training plan.</p>"""
    available_instance_count: NotRequired[
        "capo_sagemaker.types.available_instance_count.AvailableInstanceCount"
    ]
    """<p>The number of instances currently available for use in this training plan.</p>"""
    in_use_instance_count: NotRequired[
        "capo_sagemaker.types.in_use_instance_count.InUseInstanceCount"
    ]
    """<p>The number of instances currently in use from this training plan.</p>"""
    total_ultra_server_count: NotRequired[
        "capo_sagemaker.types.ultra_server_count.UltraServerCount"
    ]
    """<p>The total number of UltraServers allocated to this training plan.</p>"""
    target_resources: NotRequired[
        "capo_sagemaker.types.sage_maker_resource_names.SageMakerResourceNames"
    ]
    """<p>The target resources (e.g., training jobs, HyperPod clusters, Endpoints, Studio apps) that can use this training plan.</p> <p>Training plans are specific to their target resource.</p> <ul> <li> <p>A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.</p> </li> <li> <p>A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group.</p> </li> <li> <p>A training plan for SageMaker endpoints can be used exclusively to provide compute resources to SageMaker endpoints for model deployment.</p> </li> <li> <p>A training plan for Studio apps can be used to launch JupyterLab and Code Editor apps on reserved training plan capacity.</p> </li> </ul>"""
    reserved_capacity_summaries: NotRequired[
        "capo_sagemaker.types.reserved_capacity_summaries.ReservedCapacitySummaries"
    ]
    """<p>A list of reserved capacities associated with this training plan, including details such as instance types, counts, and availability zones.</p>"""