Class: Aws::SageMaker::Types::HyperbandStrategyConfig

Inherits:

Struct

Object
Struct
Aws::SageMaker::Types::HyperbandStrategyConfig

show all

Includes:: Aws::Structure

Defined in:: lib/aws-sdk-sagemaker/types.rb

Overview

The configuration for Hyperband, a multi-fidelity based hyperparameter tuning strategy. Hyperband uses the final and intermediate results of a training job to dynamically allocate resources to utilized hyperparameter configurations while automatically stopping under-performing configurations. This parameter should be provided only if Hyperband is selected as the StrategyConfig under the HyperParameterTuningJobConfig API.

Constant Summary collapse

SENSITIVE =

[]

Instance Attribute Summary collapse

#max_resource ⇒ Integer

The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job.
#min_resource ⇒ Integer

The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job.

Instance Attribute Details

#max_resource ⇒ `Integer`

The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTraining attempts to infer MaxResource from the following keys (if present) in [StaticsHyperParameters]:

epochs
numepochs
n-epochs
n_epochs
num_epochs

If HyperbandStrategyConfig is unable to infer a value for MaxResource, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive [early stopping decisions]. For

distributed][3: training jobs, ensure that duplicate metrics are

not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-StaticHyperParameters [2]: docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html [3]: docs.aws.amazon.com/sagemaker/latest/dg/distributed-training.html

Returns:

# File 'lib/aws-sdk-sagemaker/types.rb', line 28196

class HyperbandStrategyConfig < Struct.new(
  :min_resource,
  :max_resource)
  SENSITIVE = []
  include Aws::Structure
end

#min_resource ⇒ `Integer`

The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband.