HyperSpace: Distributed Bayesian Hyperparameter Optimization

Journal Article · 01 September 2018 · Proceedings (Symposium on Computer Architecture and High Performance Computing)

DOI: https://doi.org/10.1109/CAHPC.2018.8645954 · OSTI ID:1556107

Young, M. Todd ^[1]; Hinkle, Jacob ^[1]; Ramanathan, Arvind ^[1]; Kannan, Ramakrishnan ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

As machine learning models continue to increase in complexity, so does the potential number of free model parameters commonly known as hyperparameters. While there has been considerable progress toward finding optimal configurations of these hyperparameters, many optimization procedures are treated as black boxes. We believe optimization methods should not only return a set of optimized hyperparameters, but also give insight into the effects of model hyperparameter settings. To this end, we present HyperSpace, a parallel implementation of Bayesian sequential model-based optimization. HyperSpace leverages high performance computing (HPC) resources to better understand unknown, potentially non-convex hyperparameter search spaces. We show that it is possible to learn the dependencies between model hyperparameters through the optimization process. By partitioning large search spaces and running many optimization procedures in parallel, we also show that it is possible to discover families of good hyperparameter settings over a variety of models including unsupervised clustering, regression, and classification tasks.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1556107
• Journal Information: Proceedings (Symposium on Computer Architecture and High Performance Computing), Journal Name: Proceedings (Symposium on Computer Architecture and High Performance Computing); ISSN 1550-6533
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English