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

Inherits:
Struct
  • Object
show all
Includes:
Aws::Structure
Defined in:
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
  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.

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:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10745

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

#parameter_ranges ⇒ Types::ParameterRanges

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

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10745

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

#resource_limits ⇒ Types::ResourceLimits

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

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10745

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

#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].

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

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10745

class HyperParameterTuningJobConfig < Struct.new(
  :strategy,
  :hyper_parameter_tuning_job_objective,
  :resource_limits,
  :parameter_ranges,
  :training_job_early_stopping_type,
  :tuning_job_completion_criteria)
  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. 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][1].

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

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10745

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

#tuning_job_completion_criteria ⇒ Types::TuningJobCompletionCriteria

The tuning job’s completion criteria.

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10745

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