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

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

Note:

When making an API call, you may pass HyperParameterTuningJobConfig data as a hash:

{
  strategy: "Bayesian", # required, accepts Bayesian, Random, Hyperband
  strategy_config: {
    hyperband_strategy_config: {
      min_resource: 1,
      max_resource: 1,
    },
  },
  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.

Constant Summary collapse

SENSITIVE =
[]

Instance Attribute Summary collapse

Instance Attribute Details

#hyper_parameter_tuning_job_objectiveTypes::HyperParameterTuningJobObjective

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end

#parameter_rangesTypes::ParameterRanges

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end

#resource_limitsTypes::ResourceLimits

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end

#strategyString

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end

#strategy_configTypes::HyperParameterTuningJobStrategyConfig

The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end

#training_job_early_stopping_typeString

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on 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

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)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end

#tuning_job_completion_criteriaTypes::TuningJobCompletionCriteria

The tuning job's completion criteria.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 22551

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :strategy_config,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  SENSITIVE = []
  include Aws::Structure
end