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This research applies Constrained Bayesian Optimization to automate hyperparameter tuning specifically for minimizing the energy costs of training large machine learning models while maintaining performance thresholds. By treating energy consumption as a primary objective rather than an incidental metric, this work directly addresses the sustainability challenges inherent in scaling AI infrastructure and model sizes. It represents a significant step toward efficient ML development where environmental impact is mathematically integrated into the optimization loop itself.
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
arXiv:2407.05788v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to automate the task of hyperparameter optimization (HPO) in machine learning (ML) models with the primary objective of optimizing predictive performance on held-out data. In recent years, however, with ever-growing model sizes, the energy cost associated with model training has become an important factor for ML applications. Here we evaluate Constrained Bayesian Optimization (CBO) with the primary objective of minimizing energy consumption and subject to the constraint that the generalization performance is above some threshold. We evaluate our approach on regression and classification tasks and demonstrate that CBO achieves lower energy consumption without compromising the predictive performance of ML models.
Abstract:Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to automate the task of hyperparameter optimization (HPO) in machine learning (ML) models with the primary objective of optimizing predictive performance on held-out data. In recent years, however, with ever-growing model sizes, the energy cost associated with model training has become an important factor for ML applications. Here we evaluate Constrained Bayesian Optimization (CBO) with the primary objective of minimizing energy consumption and subject to the constraint that the generalization performance is above some threshold. We evaluate our approach on regression and classification tasks and demonstrate that CBO achieves lower energy consumption without compromising the predictive performance of ML models.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2407.05788 [cs.LG]
(or arXiv:2407.05788v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2407.05788 Focus to learn more arXiv-issued DOI via DataCite
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