HyperbandStrategyConfig
import type { HyperbandStrategyConfig } from "https://aws-api.deno.dev/v0.4/services/sagemaker.ts?docs=full";
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.
§Properties
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, HyperbandTrainingJ
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 distributive 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.