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

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

Overview

Configures a hyperparameter tuning job.

Constant Summary collapse

SENSITIVE =
[]

Instance Attribute Summary collapse

Instance Attribute Details

#hyper_parameter_tuning_job_objectiveTypes::HyperParameterTuningJobObjective

The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19299

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,
  :random_seed)
  SENSITIVE = []
  include Aws::Structure
end

#parameter_rangesTypes::ParameterRanges

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19299

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,
  :random_seed)
  SENSITIVE = []
  include Aws::Structure
end

#random_seedInteger

A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.

Returns:

  • (Integer)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19299

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,
  :random_seed)
  SENSITIVE = []
  include Aws::Structure
end

#resource_limitsTypes::ResourceLimits

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 19299

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,
  :random_seed)
  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 19299

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,
  :random_seed)
  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 19299

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,
  :random_seed)
  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 19299

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,
  :random_seed)
  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 19299

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,
  :random_seed)
  SENSITIVE = []
  include Aws::Structure
end