start_recommender ¶

def start_recommender(
    self,
    recommender_arn: "capo_personalize.types.arn.Arn",
    *,
    config_overrides: Optional[PersonalizeClientConfig] = None,
) -> "capo_personalize.types.start_recommender_response.StartRecommenderResponse":
    """<p>Starts a recommender that is INACTIVE. Starting a recommender does not create any new models, but resumes billing and automatic retraining for the recommender.</p>

    Args:
        recommender_arn: <p>The Amazon Resource Name (ARN) of the recommender to start.</p>

    Raises:
        capo_personalize.errors.invalid_input_exception.InvalidInputException: <p>Provide a valid value for the field or parameter.</p>
        capo_personalize.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
        capo_personalize.errors.resource_not_found_exception.ResourceNotFoundException: <p>Could not find the specified resource.</p>
        capo_personalize.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_personalize.types.start_recommender_request.StartRecommenderRequest]",
    ) -> OperationResponse[
        "capo_personalize.types.start_recommender_response.StartRecommenderResponse"
    ]:
        import capo_personalize._operations.amazon_personalize.start_recommender

        output, http_response = (
            capo_personalize._operations.amazon_personalize.start_recommender.start_recommender(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartRecommender")
    )
    input_: capo_personalize.types.start_recommender_request.StartRecommenderRequest = {
        "recommender_arn": recommender_arn
    }

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