create_hyper_parameter_tuning_job ¶

def create_hyper_parameter_tuning_job(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    hyper_parameter_tuning_job_name: Optional[
        "capo_sagemaker.types.hyper_parameter_tuning_job_name.HyperParameterTuningJobName"
    ] = None,
    hyper_parameter_tuning_job_config: Optional[
        "capo_sagemaker.types.hyper_parameter_tuning_job_config.HyperParameterTuningJobConfig"
    ] = None,
    training_job_definition: Optional[
        "capo_sagemaker.types.hyper_parameter_training_job_definition.HyperParameterTrainingJobDefinition"
    ] = None,
    training_job_definitions: Optional[
        "capo_sagemaker.types.hyper_parameter_training_job_definitions.HyperParameterTrainingJobDefinitions"
    ] = None,
    warm_start_config: Optional[
        "capo_sagemaker.types.hyper_parameter_tuning_job_warm_start_config.HyperParameterTuningJobWarmStartConfig"
    ] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
    autotune: Optional["capo_sagemaker.types.autotune.Autotune"] = None,
) -> "capo_sagemaker.types.create_hyper_parameter_tuning_job_response.CreateHyperParameterTuningJobResponse":
    """<p>Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.</p> <p>A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-view-compare.html#experiments-view">View Experiments, Trials, and Trial Components</a>.</p> <important> <p>Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..</p> </important>

    Args:
        hyper_parameter_tuning_job_name: <p>The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.</p>
        hyper_parameter_tuning_job_config: <p>The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html">HyperParameterTuningJobConfig</a> object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How Hyperparameter Tuning Works</a>.</p>
        training_job_definition: <p>The <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a> object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.</p>
        training_job_definitions: <p>A list of the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html">HyperParameterTrainingJobDefinition</a> objects launched for this tuning job.</p>
        warm_start_config: <p>Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.</p> <p>All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the <code>WarmStartType</code> value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.</p> <note> <p>All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.</p> </note>
        tags: <p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services Resources</a>.</p> <p>Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.</p>
        autotune: <p>Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:</p> <ul> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-ParameterRanges">ParameterRanges</a>: The names and ranges of parameters that a hyperparameter tuning job can optimize.</p> </li> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html">ResourceLimits</a>: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.</p> </li> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-TrainingJobEarlyStoppingType">TrainingJobEarlyStoppingType</a>: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.</p> </li> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-RetryStrategy">RetryStrategy</a>: The number of times to retry a training job.</p> </li> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html">Strategy</a>: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.</p> </li> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ConvergenceDetected.html">ConvergenceDetected</a>: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.</p> </li> </ul>

    Raises:
        capo_sagemaker.errors.resource_in_use.ResourceInUse: <p>Resource being accessed is in use.</p>
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.create_hyper_parameter_tuning_job_request.CreateHyperParameterTuningJobRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_hyper_parameter_tuning_job_response.CreateHyperParameterTuningJobResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.create_hyper_parameter_tuning_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateHyperParameterTuningJob")
    )
    input_: capo_sagemaker.types.create_hyper_parameter_tuning_job_request.CreateHyperParameterTuningJobRequest = {}
    if hyper_parameter_tuning_job_name is not None:
        input_["hyper_parameter_tuning_job_name"] = hyper_parameter_tuning_job_name
    if hyper_parameter_tuning_job_config is not None:
        input_["hyper_parameter_tuning_job_config"] = (
            hyper_parameter_tuning_job_config
        )
    if training_job_definition is not None:
        input_["training_job_definition"] = training_job_definition
    if training_job_definitions is not None:
        input_["training_job_definitions"] = training_job_definitions
    if warm_start_config is not None:
        input_["warm_start_config"] = warm_start_config
    if tags is not None:
        input_["tags"] = tags
    if autotune is not None:
        input_["autotune"] = autotune

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