Assessor: Early Stopping

In HPO, some hyperparameter sets may have obviously poor performance and it will be unnecessary to finish the evaluation. This is called early stopping, and in NNI early stopping algorithms are called assessors.

An assessor monitors intermediate results of each trial. If a trial is predicted to produce suboptimal final result, the assessor will stop that trial immediately, to save computing resources for other hyperparameter sets.

As introduced in quickstart tutorial, a trial is the evaluation process of a hyperparameter set, and intermediate results are reported with nni.report_intermediate_result() API in trial code. Typically, intermediate results are accuracy or loss metrics of each epoch.

Using an assessor will increase the efficiency of computing resources, but may slightly reduce the predicition accuracy of tuners. It is recommended to use an assessor when computing resources are insufficient.

Common Usage

The usage of assessors are similar to tuners.

To use a built-in assessor you need to specify its name and arguments: = 'Medianstop'
config.assessor.class_args = {'optimize_mode': 'maximize'}

Built-in Assessors


Brief Introduction of Algorithm

Median Stop

Stop if the hyperparameter set performs worse than median at any step.

Curve Fitting

Stop if the learning curve will likely converge to suboptimal result.