delete_ml_endpoint ¶

def delete_ml_endpoint(
    self,
    id: str,
    *,
    config_overrides: Optional[neptunedataClientConfig] = None,
    neptune_iam_role_arn: Optional[str] = None,
    clean: Optional[bool] = None,
) -> "capo_neptunedata.types.delete_ml_endpoint_output.DeleteMLEndpointOutput":
    """<p>Cancels the creation of a Neptune ML inference endpoint. See <a href="https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning-api-endpoints.html">Managing inference endpoints using the endpoints command</a>.</p> <p>When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the <a href="https://docs.aws.amazon.com/neptune/latest/userguide/iam-dp-actions.html#deletemlendpoint">neptune-db:DeleteMLEndpoint</a> IAM action in that cluster.</p>

    Args:
        id: <p>The unique identifier of the inference endpoint.</p>
        neptune_iam_role_arn: <p>The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.</p>
        clean: <p>If this flag is set to <code>TRUE</code>, all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is <code>FALSE</code>.</p>

    Raises:
        capo_neptunedata.errors.bad_request_exception.BadRequestException: <p>Raised when a request is submitted that cannot be processed.</p>
        capo_neptunedata.errors.client_timeout_exception.ClientTimeoutException: <p>Raised when a request timed out in the client.</p>
        capo_neptunedata.errors.constraint_violation_exception.ConstraintViolationException: <p>Raised when a value in a request field did not satisfy required constraints.</p>
        capo_neptunedata.errors.illegal_argument_exception.IllegalArgumentException: <p>Raised when an argument in a request is not supported.</p>
        capo_neptunedata.errors.invalid_argument_exception.InvalidArgumentException: <p>Raised when an argument in a request has an invalid value.</p>
        capo_neptunedata.errors.invalid_parameter_exception.InvalidParameterException: <p>Raised when a parameter value is not valid.</p>
        capo_neptunedata.errors.missing_parameter_exception.MissingParameterException: <p>Raised when a required parameter is missing.</p>
        capo_neptunedata.errors.ml_resource_not_found_exception.MLResourceNotFoundException: <p>Raised when a specified machine-learning resource could not be found.</p>
        capo_neptunedata.errors.preconditions_failed_exception.PreconditionsFailedException: <p>Raised when a precondition for processing a request is not satisfied.</p>
        capo_neptunedata.errors.too_many_requests_exception.TooManyRequestsException: <p>Raised when the number of requests being processed exceeds the limit.</p>
        capo_neptunedata.errors.unsupported_operation_exception.UnsupportedOperationException: <p>Raised when a request attempts to initiate an operation that is not supported.</p>
        capo_neptunedata.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_neptunedata.types.delete_ml_endpoint_input.DeleteMLEndpointInput]",
    ) -> OperationResponse[
        "capo_neptunedata.types.delete_ml_endpoint_output.DeleteMLEndpointOutput"
    ]:
        import capo_neptunedata._operations.amazon_neptune_dataplane.delete_ml_endpoint

        output, http_response = (
            capo_neptunedata._operations.amazon_neptune_dataplane.delete_ml_endpoint.delete_ml_endpoint(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DeleteMLEndpoint")
    )
    input_: capo_neptunedata.types.delete_ml_endpoint_input.DeleteMLEndpointInput = {
        "id": id
    }
    if neptune_iam_role_arn is not None:
        input_["neptune_iam_role_arn"] = neptune_iam_role_arn
    if clean is not None:
        input_["clean"] = clean

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