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HyperbandStrategyConfig

Source code in docs_src/capo_sagemaker/types/hyperband_strategy_config.py 
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class HyperbandStrategyConfig(TypedDict, closed=True):
    min_resource: NotRequired[
        "capo_sagemaker.types.hyperband_strategy_min_resource.HyperbandStrategyMinResource"
    ]
    """<p>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 <code>MinResource</code> has not been reached, the training job is not stopped by <code>Hyperband</code>.</p>"""
    max_resource: NotRequired[
        "capo_sagemaker.types.hyperband_strategy_max_resource.HyperbandStrategyMaxResource"
    ]
    """<p>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 <code>MaxResource</code> value, it is stopped. If a value for <code>MaxResource</code> is not provided, and <code>Hyperband</code> is selected as the hyperparameter tuning strategy, <code>HyperbandTraining</code> attempts to infer <code>MaxResource</code> from the following keys (if present) in <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-StaticHyperParameters">StaticsHyperParameters</a>:</p> <ul> <li> <p> <code>epochs</code> </p> </li> <li> <p> <code>numepochs</code> </p> </li> <li> <p> <code>n-epochs</code> </p> </li> <li> <p> <code>n_epochs</code> </p> </li> <li> <p> <code>num_epochs</code> </p> </li> </ul> <p>If <code>HyperbandStrategyConfig</code> is unable to infer a value for <code>MaxResource</code>, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html">early stopping decisions</a>. For <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-training.html">distributed</a> 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. </p>"""

max_resource instance-attribute 

max_resource: NotRequired[HyperbandStrategyMaxResource]

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 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.

min_resource instance-attribute 

min_resource: NotRequired[HyperbandStrategyMinResource]

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.