CreateOptimizationJobRequest ¶

class CreateOptimizationJobRequest(TypedDict, closed=True):
    optimization_job_name: NotRequired["capo_sagemaker.types.entity_name.EntityName"]
    """<p>A custom name for the new optimization job.</p>"""
    role_arn: NotRequired["capo_sagemaker.types.role_arn.RoleArn"]
    """<p>The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf. </p> <p>During model optimization, Amazon SageMaker AI needs your permission to:</p> <ul> <li> <p>Read input data from an S3 bucket</p> </li> <li> <p>Write model artifacts to an S3 bucket</p> </li> <li> <p>Write logs to Amazon CloudWatch Logs</p> </li> <li> <p>Publish metrics to Amazon CloudWatch</p> </li> </ul> <p>You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the <code>iam:PassRole</code> permission. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker AI Roles.</a> </p>"""
    model_source: NotRequired[
        "capo_sagemaker.types.optimization_job_model_source.OptimizationJobModelSource"
    ]
    """<p>The location of the source model to optimize with an optimization job.</p>"""
    deployment_instance_type: NotRequired[
        "capo_sagemaker.types.optimization_job_deployment_instance_type.OptimizationJobDeploymentInstanceType"
    ]
    """<p>The type of instance that hosts the optimized model that you create with the optimization job.</p>"""
    max_instance_count: NotRequired[
        "capo_sagemaker.types.optimization_job_max_instance_count.OptimizationJobMaxInstanceCount"
    ]
    """<p>The maximum number of instances to use for the optimization job.</p>"""
    optimization_environment: NotRequired[
        "capo_sagemaker.types.optimization_job_environment_variables.OptimizationJobEnvironmentVariables"
    ]
    """<p>The environment variables to set in the model container.</p>"""
    optimization_configs: NotRequired[
        "capo_sagemaker.types.optimization_configs.OptimizationConfigs"
    ]
    """<p>Settings for each of the optimization techniques that the job applies.</p>"""
    output_config: NotRequired[
        "capo_sagemaker.types.optimization_job_output_config.OptimizationJobOutputConfig"
    ]
    """<p>Details for where to store the optimized model that you create with the optimization job.</p>"""
    stopping_condition: NotRequired[
        "capo_sagemaker.types.stopping_condition.StoppingCondition"
    ]
    tags: NotRequired["capo_sagemaker.types.tag_list.TagList"]
    """<p>A list of key-value pairs associated with the optimization job. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services resources</a> in the <i>Amazon Web Services General Reference Guide</i>.</p>"""
    vpc_config: NotRequired[
        "capo_sagemaker.types.optimization_vpc_config.OptimizationVpcConfig"
    ]
    """<p>A VPC in Amazon VPC that your optimized model has access to.</p>"""
    training_plan_arns: NotRequired[
        "capo_sagemaker.types.optimization_job_training_plan_arns.OptimizationJobTrainingPlanArns"
    ]
    """<p>The Amazon Resource Name (ARN) of the training plan to use for this optimization job.</p> <p>When you use reserved capacity from a training plan, the optimization job runs on that reserved capacity instead of on-demand capacity. If you omit this field, the job uses on-demand capacity. You can specify at most one training plan.</p> <p>For more information about how to reserve GPU capacity for your optimization jobs using Amazon SageMaker Training Plans, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html">Reserve capacity with training plans</a>.</p>"""