HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization

• URL: http://arxiv.org/abs/2110.01698v1
• Date: Mon, 4 Oct 2021 20:14:22 GMT
• Title: HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization
• Authors: Vincent Dumont, Casey Garner, Anuradha Trivedi, Chelsea Jones, Vidya Ganapati, Juliane Mueller, Talita Perciano, Mariam Kiran, and Marc Day
• Abstract summary: HYPPO uses adaptive surrogate models and accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction.
• Score: 0.2844198651668139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. Using asynchronous nested parallelism, we are able to significantly alleviate the computational burden of training complex architectures and quantifying the uncertainty. HYPPO is implemented in Python and can be used with both TensorFlow and PyTorch libraries. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction. Finally, we show that (1) we can reduce by an order of magnitude the number of evaluations necessary to find the most optimal region in the hyperparameter space and (2) we can reduce by two orders of magnitude the throughput for such HPO process to complete.

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