list_model_quality_job_definitions ¶

def list_model_quality_job_definitions(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    endpoint_name: Optional[
        "capo_sagemaker.types.endpoint_name.EndpointName"
    ] = None,
    sort_by: Optional[
        "capo_sagemaker.types.monitoring_job_definition_sort_key.MonitoringJobDefinitionSortKey"
    ] = None,
    sort_order: Optional["capo_sagemaker.types.sort_order.SortOrder"] = None,
    next_token: Optional["capo_sagemaker.types.next_token.NextToken"] = None,
    max_results: Optional["capo_sagemaker.types.max_results.MaxResults"] = None,
    name_contains: Optional[
        "capo_sagemaker.types.name_contains.NameContains"
    ] = None,
    creation_time_before: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    creation_time_after: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
) -> "capo_sagemaker.types.list_model_quality_job_definitions_response.ListModelQualityJobDefinitionsResponse":
    """<p>Gets a list of model quality monitoring job definitions in your account.</p>

    Args:
        endpoint_name: <p>A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.</p>
        sort_by: <p>The field to sort results by. The default is <code>CreationTime</code>.</p>
        sort_order: <p>Whether to sort the results in <code>Ascending</code> or <code>Descending</code> order. The default is <code>Descending</code>.</p>
        next_token: <p>If the result of the previous <code>ListModelQualityJobDefinitions</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.</p>
        max_results: <p>The maximum number of results to return in a call to <code>ListModelQualityJobDefinitions</code>.</p>
        name_contains: <p>A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.</p>
        creation_time_before: <p>A filter that returns only model quality monitoring job definitions created before the specified time.</p>
        creation_time_after: <p>A filter that returns only model quality monitoring job definitions created after the specified time.</p>

    Raises:
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.list_model_quality_job_definitions_request.ListModelQualityJobDefinitionsRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.list_model_quality_job_definitions_response.ListModelQualityJobDefinitionsResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.list_model_quality_job_definitions

        output, http_response = (
            capo_sagemaker._operations.sage_maker.list_model_quality_job_definitions.list_model_quality_job_definitions(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListModelQualityJobDefinitions")
    )
    input_: capo_sagemaker.types.list_model_quality_job_definitions_request.ListModelQualityJobDefinitionsRequest = {}
    if endpoint_name is not None:
        input_["endpoint_name"] = endpoint_name
    if sort_by is not None:
        input_["sort_by"] = sort_by
    if sort_order is not None:
        input_["sort_order"] = sort_order
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if name_contains is not None:
        input_["name_contains"] = name_contains
    if creation_time_before is not None:
        input_["creation_time_before"] = creation_time_before
    if creation_time_after is not None:
        input_["creation_time_after"] = creation_time_after

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