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describe_model_version

describe_model_version(
    model_name: ModelName,
    model_version: ModelVersion,
    *,
    config_overrides: Optional[LookoutEquipmentClientConfig] = None,
) -> DescribeModelVersionResponse

Retrieves information about a specific machine learning model version.

Parameters:

  • model_name (ModelName) –

    The name of the machine learning model that this version belongs to.

  • model_version (ModelVersion) –

    The version of the machine learning model.

Raises:

  • AccessDeniedException –

    The request could not be completed because you do not have access to the resource.

  • InternalServerException –

    Processing of the request has failed because of an unknown error, exception or failure.

  • ResourceNotFoundException –

    The resource requested could not be found. Verify the resource ID and retry your request.

  • ThrottlingException –

    The request was denied due to request throttling.

  • ValidationException –

    The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized.

  • UnknownServiceError –

    The service returned an error code this client does not model.

Source code in docs_src/capo_lookoutequipment/_services/lookout_equipment.py
def describe_model_version(
    self,
    model_name: "capo_lookoutequipment.types.model_name.ModelName",
    model_version: "capo_lookoutequipment.types.model_version.ModelVersion",
    *,
    config_overrides: Optional[LookoutEquipmentClientConfig] = None,
) -> "capo_lookoutequipment.types.describe_model_version_response.DescribeModelVersionResponse":
    """<p>Retrieves information about a specific machine learning model version.</p>

    Args:
        model_name: <p>The name of the machine learning model that this version belongs to.</p>
        model_version: <p>The version of the machine learning model.</p>

    Raises:
        capo_lookoutequipment.errors.access_denied_exception.AccessDeniedException: <p>The request could not be completed because you do not have access to the resource. </p>
        capo_lookoutequipment.errors.internal_server_exception.InternalServerException: <p> Processing of the request has failed because of an unknown error, exception or failure. </p>
        capo_lookoutequipment.errors.resource_not_found_exception.ResourceNotFoundException: <p> The resource requested could not be found. Verify the resource ID and retry your request. </p>
        capo_lookoutequipment.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_lookoutequipment.errors.validation_exception.ValidationException: <p> The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized. </p>
        capo_lookoutequipment.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_lookoutequipment.types.describe_model_version_request.DescribeModelVersionRequest]",
    ) -> OperationResponse[
        "capo_lookoutequipment.types.describe_model_version_response.DescribeModelVersionResponse"
    ]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.describe_model_version

        output, http_response = (
            capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.describe_model_version.describe_model_version(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeModelVersion")
    )
    input_: capo_lookoutequipment.types.describe_model_version_request.DescribeModelVersionRequest = {
        "model_name": model_name,
        "model_version": model_version,
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output