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

Returns:

• (Types::HyperParameterTuningJobObjective)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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

Returns:

• (Types::ParameterRanges)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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.

Returns:

• (Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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

Returns:

• (Types::ResourceLimits)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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

Returns:

• (String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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`.

Returns:

• (Types::HyperParameterTuningJobStrategyConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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

Returns:

• (String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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.

Returns:

• (Types::TuningJobCompletionCriteria)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23303

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