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

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::HyperParameterTuningJobConfig

Includes:

Aws::Structure

Defined in:

lib/aws-sdk-sagemaker/types.rb

Overview

Configures a hyperparameter tuning job.

See Also:

• AWS API Documentation

Constant Summary

SENSITIVE =

[]

Instance Attribute Summary

• #hyper_parameter_tuning_job_objective ⇒ Types::HyperParameterTuningJobObjective

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

• #parameter_ranges ⇒ Types::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.

• #random_seed ⇒ Integer

  A value used to initialize a pseudo-random number generator.

• #resource_limits ⇒ Types::ResourceLimits

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

• #strategy ⇒ String

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

• #strategy_config ⇒ Types::HyperParameterTuningJobStrategyConfig

  The configuration for the Hyperband optimization strategy.

• #training_job_early_stopping_type ⇒ 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.

Instance Attribute Details

#hyper_parameter_tuning_job_objective ⇒ Types::HyperParameterTuningJobObjective

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

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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_ranges ⇒ Types::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.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ParameterRanges.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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_seed ⇒ Integer

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.

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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_limits ⇒ Types::ResourceLimits

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

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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 ⇒ String

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].

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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_config ⇒ Types::HyperParameterTuningJobStrategyConfig

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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_type ⇒ String

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][1].

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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_criteria ⇒ Types::TuningJobCompletionCriteria

The tuning job’s completion criteria.

# File 'lib/aws-sdk-sagemaker/types.rb', line 27656

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