Builder

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

The HyperParameterTuningJobConfig 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 How Hyperparameter Tuning Works.

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.

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 Tagging Amazon Web Services Resources.

The HyperParameterTrainingJobDefinition 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.

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

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.

construct an aws.sdk.kotlin.services.sagemaker.model.Autotune inside the given block

construct an aws.sdk.kotlin.services.sagemaker.model.HyperParameterTuningJobConfig inside the given block

construct an aws.sdk.kotlin.services.sagemaker.model.HyperParameterTrainingJobDefinition inside the given block

construct an aws.sdk.kotlin.services.sagemaker.model.HyperParameterTuningJobWarmStartConfig inside the given block