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.
Contents
- MaxResource
-
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
MaxResourcevalue, it is stopped. If a value forMaxResourceis not provided, andHyperbandis selected as the hyperparameter tuning strategy,HyperbandTrainingattempts to inferMaxResourcefrom the following keys (if present) in StaticsHyperParameters:-
epochs -
numepochs -
n-epochs -
n_epochs -
num_epochs
If
HyperbandStrategyConfigis unable to infer a value forMaxResource, 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.Type: Integer
Valid Range: Minimum value of 1.
Required: No
-
- MinResource
-
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
MinResourcehas not been reached, the training job is not stopped byHyperband.Type: Integer
Valid Range: Minimum value of 1.
Required: No
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following:
