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delete_dataset 

delete_dataset(
    dataset_name: DatasetIdentifier,
    *,
    config_overrides: Optional[LookoutEquipmentClientConfig] = None,
) -> None

Deletes a dataset and associated artifacts. The operation will check to see if any inference scheduler or data ingestion job is currently using the dataset, and if there isn't, the dataset, its metadata, and any associated data stored in S3 will be deleted. This does not affect any models that used this dataset for training and evaluation, but does prevent it from being used in the future.

Parameters:

Raises:

  • AccessDeniedException –

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

  • ConflictException –

    The request could not be completed due to a conflict with the current state of the target 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 
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def delete_dataset(
    self,
    dataset_name: "capo_lookoutequipment.types.dataset_identifier.DatasetIdentifier",
    *,
    config_overrides: Optional[LookoutEquipmentClientConfig] = None,
) -> None:
    """<p> Deletes a dataset and associated artifacts. The operation will check to see if any inference scheduler or data ingestion job is currently using the dataset, and if there isn't, the dataset, its metadata, and any associated data stored in S3 will be deleted. This does not affect any models that used this dataset for training and evaluation, but does prevent it from being used in the future. </p>

    Args:
        dataset_name: <p>The name of the dataset to be deleted. </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.delete_dataset_request.DeleteDatasetRequest]",
    ) -> OperationResponse[None]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.delete_dataset

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DeleteDataset")
    )
    input_: capo_lookoutequipment.types.delete_dataset_request.DeleteDatasetRequest = {
        "dataset_name": dataset_name
    }

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