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Training deep neural networks (DNNs) is becoming increasingly more resource-and energy-intensive every year. Unfortunately, existing works primarily focuson optimizing DNN training for faster completion, often without considering theimpact on energy efficiency.  In this paper, we observe that common practices to improve trainingperformance can often lead to inefficient energy usage. More importantly, wedemonstrate that there is a tradeoff between energy consumption and performanceoptimization. To this end, we propose Zeus, an optimization framework tonavigate this tradeoff by automatically finding optimal job- and GPU-levelconfigurations for recurring DNN training jobs. Zeus uses an onlineexploration-exploitation approach in conjunction with just-in-time energyprofiling, averting the need for expensive offline measurements, while adaptingto data drifts over time. Our evaluation shows that Zeus can improve the energyefficiency of DNN training by 15.3%-75.8% for diverse workloads.

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training