Modern learning models are characterized by large hyperparameter spaces. In order to adequately explore these large spaces, we must evaluate a large number of configurations, typically orders of magnitude more configurations than available parallel workers. Given the growing costs of model training, we would ideally like to perform this search in roughly the same wall-clock time needed to train a single model. In this work, we tackle this challenge by introducing ASHA, a simple and robust hyperparameter tuning algorithm with solid theoretical underpinnings that exploits parallelism and aggressive early-stopping. Our extensive empirical results show that ASHA slightly outperforms Fabolas and Population Based Tuning, state-of-the hyperparameter tuning methods; scales linearly with the number of workers in distributed settings; converges to a high quality configuration in half the time taken by Vizier (Google’s internal hyperparameter tuning service) in an experiment with 500 workers; and matches the performance of complex neural architecture search methods in under 2x the time to train a single model.
Ameet Talwalkar is co-founder and chief scientist at Determined AI and an assistant professor in the Machine Learning Department at Carnegie Mellon University. His primary interests are in the field of statistical machine learning, including problems at the intersection of systems and learning. He helped to create the SysML conference, led the initial development of the MLlib project in Apache Spark, is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press), and teaches an award-winning MOOC called Distributed Machine Learning with Apache Spark (edX).
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