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:

(Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 23882

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`.