SageMaker / Client / describe_inference_component

describe_inference_component

SageMaker.Client.describe_inference_component(**kwargs)

Returns information about an inference component.

See also: AWS API Documentation

Request Syntax

response = client.describe_inference_component(
    InferenceComponentName='string'
)

Parameters:

InferenceComponentName (string) –

[REQUIRED]

The name of the inference component.

Return type:

dict

Returns:

Response Syntax

{
    'InferenceComponentName': 'string',
    'InferenceComponentArn': 'string',
    'EndpointName': 'string',
    'EndpointArn': 'string',
    'VariantName': 'string',
    'FailureReason': 'string',
    'Specification': {
        'ModelName': 'string',
        'Container': {
            'DeployedImage': {
                'SpecifiedImage': 'string',
                'ResolvedImage': 'string',
                'ResolutionTime': datetime(2015, 1, 1)
            },
            'ArtifactUrl': 'string',
            'Environment': {
                'string': 'string'
            }
        },
        'StartupParameters': {
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123
        },
        'ComputeResourceRequirements': {
            'NumberOfCpuCoresRequired': ...,
            'NumberOfAcceleratorDevicesRequired': ...,
            'MinMemoryRequiredInMb': 123,
            'MaxMemoryRequiredInMb': 123
        },
        'BaseInferenceComponentName': 'string'
    },
    'RuntimeConfig': {
        'DesiredCopyCount': 123,
        'CurrentCopyCount': 123
    },
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'InferenceComponentStatus': 'InService'|'Creating'|'Updating'|'Failed'|'Deleting',
    'LastDeploymentConfig': {
        'RollingUpdatePolicy': {
            'MaximumBatchSize': {
                'Type': 'COPY_COUNT'|'CAPACITY_PERCENT',
                'Value': 123
            },
            'WaitIntervalInSeconds': 123,
            'MaximumExecutionTimeoutInSeconds': 123,
            'RollbackMaximumBatchSize': {
                'Type': 'COPY_COUNT'|'CAPACITY_PERCENT',
                'Value': 123
            }
        },
        'AutoRollbackConfiguration': {
            'Alarms': [
                {
                    'AlarmName': 'string'
                },
            ]
        }
    }
}

Response Structure

• (dict) –

  • InferenceComponentName (string) –

    The name of the inference component.

  • InferenceComponentArn (string) –

    The Amazon Resource Name (ARN) of the inference component.

  • EndpointName (string) –

    The name of the endpoint that hosts the inference component.

  • EndpointArn (string) –

    The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.

  • VariantName (string) –

    The name of the production variant that hosts the inference component.

  • FailureReason (string) –

    If the inference component status is Failed, the reason for the failure.

  • Specification (dict) –

    Details about the resources that are deployed with this inference component.

    • ModelName (string) –

      The name of the SageMaker AI model object that is deployed with the inference component.

    • Container (dict) –

      Details about the container that provides the runtime environment for the model that is deployed with the inference component.

      • DeployedImage (dict) –

        Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.

        If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

        • SpecifiedImage (string) –

          The image path you specified when you created the model.

        • ResolvedImage (string) –

          The specific digest path of the image hosted in this ProductionVariant.

        • ResolutionTime (datetime) –

          The date and time when the image path for the model resolved to the ResolvedImage

      • ArtifactUrl (string) –

        The Amazon S3 path where the model artifacts are stored.

      • Environment (dict) –

        The environment variables to set in the Docker container.

        • (string) –

          • (string) –

    • StartupParameters (dict) –

      Settings that take effect while the model container starts up.

      • ModelDataDownloadTimeoutInSeconds (integer) –

        The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

      • ContainerStartupHealthCheckTimeoutInSeconds (integer) –

        The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

    • ComputeResourceRequirements (dict) –

      The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

      • NumberOfCpuCoresRequired (float) –

        The number of CPU cores to allocate to run a model that you assign to an inference component.

      • NumberOfAcceleratorDevicesRequired (float) –

        The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.

      • MinMemoryRequiredInMb (integer) –

        The minimum MB of memory to allocate to run a model that you assign to an inference component.

      • MaxMemoryRequiredInMb (integer) –

        The maximum MB of memory to allocate to run a model that you assign to an inference component.

    • BaseInferenceComponentName (string) –

      The name of the base inference component that contains this inference component.

  • RuntimeConfig (dict) –

    Details about the runtime settings for the model that is deployed with the inference component.

    • DesiredCopyCount (integer) –

      The number of runtime copies of the model container that you requested to deploy with the inference component.

    • CurrentCopyCount (integer) –

      The number of runtime copies of the model container that are currently deployed.

  • CreationTime (datetime) –

    The time when the inference component was created.

  • LastModifiedTime (datetime) –

    The time when the inference component was last updated.

  • InferenceComponentStatus (string) –

    The status of the inference component.

  • LastDeploymentConfig (dict) –

    The deployment and rollback settings that you assigned to the inference component.

    • RollingUpdatePolicy (dict) –

      Specifies a rolling deployment strategy for updating a SageMaker AI endpoint.

      • MaximumBatchSize (dict) –

        The batch size for each rolling step in the deployment process. For each step, SageMaker AI provisions capacity on the new endpoint fleet, routes traffic to that fleet, and terminates capacity on the old endpoint fleet. The value must be between 5% to 50% of the copy count of the inference component.

        • Type (string) –

          Specifies the endpoint capacity type.

          COPY_COUNT

          The endpoint activates based on the number of inference component copies.

          CAPACITY_PERCENT

          The endpoint activates based on the specified percentage of capacity.

        • Value (integer) –

          Defines the capacity size, either as a number of inference component copies or a capacity percentage.

      • WaitIntervalInSeconds (integer) –

        The length of the baking period, during which SageMaker AI monitors alarms for each batch on the new fleet.

      • MaximumExecutionTimeoutInSeconds (integer) –

        The time limit for the total deployment. Exceeding this limit causes a timeout.

      • RollbackMaximumBatchSize (dict) –

        The batch size for a rollback to the old endpoint fleet. If this field is absent, the value is set to the default, which is 100% of the total capacity. When the default is used, SageMaker AI provisions the entire capacity of the old fleet at once during rollback.

        • Type (string) –

          Specifies the endpoint capacity type.

          COPY_COUNT

          The endpoint activates based on the number of inference component copies.

          CAPACITY_PERCENT

          The endpoint activates based on the specified percentage of capacity.

        • Value (integer) –

          Defines the capacity size, either as a number of inference component copies or a capacity percentage.

    • AutoRollbackConfiguration (dict) –

      Automatic rollback configuration for handling endpoint deployment failures and recovery.

      • Alarms (list) –

        List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.

        • (dict) –

          An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

          • AlarmName (string) –

            The name of a CloudWatch alarm in your account.