class Aws::SageMaker::Types::HyperParameterTuningJobConfig

Configures a hyperparameter tuning job.

@note When making an API call, you may pass HyperParameterTuningJobConfig

data as a hash:

    {
      strategy: "Bayesian", # required, accepts Bayesian, Random
      hyper_parameter_tuning_job_objective: {
        type: "Maximize", # required, accepts Maximize, Minimize
        metric_name: "MetricName", # required
      },
      resource_limits: { # required
        max_number_of_training_jobs: 1, # required
        max_parallel_training_jobs: 1, # required
      },
      parameter_ranges: {
        integer_parameter_ranges: [
          {
            name: "ParameterKey", # required
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
            scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
          },
        ],
        continuous_parameter_ranges: [
          {
            name: "ParameterKey", # required
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
            scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
          },
        ],
        categorical_parameter_ranges: [
          {
            name: "ParameterKey", # required
            values: ["ParameterValue"], # required
          },
        ],
      },
      training_job_early_stopping_type: "Off", # accepts Off, Auto
      tuning_job_completion_criteria: {
        target_objective_metric_value: 1.0, # required
      },
    }

@!attribute [rw] strategy

Specifies how hyperparameter tuning chooses the combinations of
hyperparameter values to use for the training job it launches. To
use the Bayesian search strategy, set this to `Bayesian`. To
randomly search, set it to `Random`. For information about search
strategies, see [How Hyperparameter Tuning Works][1].

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html
@return [String]

@!attribute [rw] hyper_parameter_tuning_job_objective

The HyperParameterTuningJobObjective object that specifies the
objective metric for this tuning job.
@return [Types::HyperParameterTuningJobObjective]

@!attribute [rw] resource_limits

The ResourceLimits object that specifies the maximum number of
training jobs and parallel training jobs for this tuning job.
@return [Types::ResourceLimits]

@!attribute [rw] parameter_ranges

The ParameterRanges object that specifies the ranges of
hyperparameters that this tuning job searches.
@return [Types::ParameterRanges]

@!attribute [rw] training_job_early_stopping_type

Specifies whether to use early stopping for training jobs launched
by the hyperparameter tuning job. This can be one of the following
values (the default value is `OFF`):

OFF

: Training jobs launched by the hyperparameter tuning job do not use
  early stopping.

AUTO

: Amazon SageMaker stops training jobs launched by the
  hyperparameter tuning job when they are unlikely to perform better
  than previously completed training jobs. For more information, see
  [Stop Training Jobs Early][1].

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html
@return [String]

@!attribute [rw] tuning_job_completion_criteria

The tuning job's completion criteria.
@return [Types::TuningJobCompletionCriteria]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/HyperParameterTuningJobConfig AWS API Documentation

Constants

SENSITIVE