A Novice's Guide to Hyperparameter Optimization at Scale

Despite the tremendous success of machine learning (ML), modern algorithms still depend on a variety of free non-trainable hyperparameters. Ultimately, our ability to select quality hyperparameters governs the performance for a given model. In the past, and even some currently, hyperparameters were hand selected through trial and error. An entire field has been dedicated to improving this selection process; it is referred to as hyperparameter optimization (HPO). Inherently, HPO requires testing many different hyperparameter configurations and as a result can benefit tremendously from massively parallel resources like the Perlmutter system we are building at the National Energy Research Scientific Computing Center (NERSC).