update_inference_scheduler ¶

def update_inference_scheduler(
    self,
    inference_scheduler_name: "capo_lookoutequipment.types.inference_scheduler_identifier.InferenceSchedulerIdentifier",
    *,
    config_overrides: Optional[LookoutEquipmentClientConfig] = None,
    data_delay_offset_in_minutes: Optional[
        "capo_lookoutequipment.types.data_delay_offset_in_minutes.DataDelayOffsetInMinutes"
    ] = None,
    data_upload_frequency: Optional[
        "capo_lookoutequipment.types.data_upload_frequency.DataUploadFrequency"
    ] = None,
    data_input_configuration: Optional[
        "capo_lookoutequipment.types.inference_input_configuration.InferenceInputConfiguration"
    ] = None,
    data_output_configuration: Optional[
        "capo_lookoutequipment.types.inference_output_configuration.InferenceOutputConfiguration"
    ] = None,
    role_arn: Optional[
        "capo_lookoutequipment.types.iam_role_arn.IamRoleArn"
    ] = None,
) -> None:
    """<p>Updates an inference scheduler. </p>

    Args:
        inference_scheduler_name: <p>The name of the inference scheduler to be updated. </p>
        data_delay_offset_in_minutes: <p> A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if you select an offset delay time of five minutes, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.</p>
        data_upload_frequency: <p>How often data is uploaded to the source S3 bucket for the input data. The value chosen is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes. </p>
        data_input_configuration: <p> Specifies information for the input data for the inference scheduler, including delimiter, format, and dataset location. </p>
        data_output_configuration: <p> Specifies information for the output results from the inference scheduler, including the output S3 location. </p>
        role_arn: <p> The Amazon Resource Name (ARN) of a role with permission to access the data source for the inference scheduler. </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.conflict_exception.ConflictException: <p> The request could not be completed due to a conflict with the current state of the target 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.update_inference_scheduler_request.UpdateInferenceSchedulerRequest]",
    ) -> OperationResponse[None]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.update_inference_scheduler

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateInferenceScheduler")
    )
    input_: capo_lookoutequipment.types.update_inference_scheduler_request.UpdateInferenceSchedulerRequest = {
        "inference_scheduler_name": inference_scheduler_name
    }
    if data_delay_offset_in_minutes is not None:
        input_["data_delay_offset_in_minutes"] = data_delay_offset_in_minutes
    if data_upload_frequency is not None:
        input_["data_upload_frequency"] = data_upload_frequency
    if data_input_configuration is not None:
        input_["data_input_configuration"] = data_input_configuration
    if data_output_configuration is not None:
        input_["data_output_configuration"] = data_output_configuration
    if role_arn is not None:
        input_["role_arn"] = role_arn

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