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Class: Aws::SageMaker::Types::HyperParameterTuningJobConfig

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing HyperParameterTuningJobConfig as input to an Aws::Client method, you can use a vanilla 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
  },
}

Configures a hyperparameter tuning job.

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#hyper_parameter_tuning_job_objective ⇒ Types::HyperParameterTuningJobObjective

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

Returns:

#parameter_ranges ⇒ Types::ParameterRanges

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

Returns:

#resource_limits ⇒ Types::ResourceLimits

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

Returns:

#strategy ⇒ String

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.

Returns:

  • (String) —

    Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches.

#training_job_early_stopping_type ⇒ String

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.

Returns:

  • (String) —

    Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.

#tuning_job_completion_criteria ⇒ Types::TuningJobCompletionCriteria

The tuning job\'s completion criteria.

Returns: