  Hyperparameter optimization (HPO) and neural architecture search (NAS) are
methods of choice to obtain the best-in-class machine learning models, but in
practice they can be costly to run. When models are trained on large datasets,
tuning them with HPO or NAS rapidly becomes prohibitively expensive for
practitioners, even when efficient multi-fidelity methods are employed. We
propose an approach to tackle the challenge of tuning machine learning models
trained on large datasets with limited computational resources. Our approach,
named PASHA, extends ASHA and is able to dynamically allocate maximum resources
for the tuning procedure depending on the need. The experimental comparison
shows that PASHA identifies well-performing hyperparameter configurations and
architectures while consuming significantly fewer computational resources than
ASHA.
