This work examines the effects of variations in machine learning training
regimes and learning paradigms on the corresponding energy consumption. While
increasing data availability and innovation in high-performance hardware fuels
the training of sophisticated models, it also supports the fading perception of
energy consumption and carbon emission. Therefore, the goal of this work is to
create awareness about the energy impact of general training parameters and
processes, from learning rate over batch size to knowledge transfer. Multiple
setups with different hyperparameter initializations are evaluated on two
different hardware configurations to obtain meaningful results. Experiments on
pretraining and multitask training are conducted on top of the baseline results
to determine their potential towards sustainable machine learning.

本研究旨在探究机器学习训练模式和学习范式的变化对应的能源消耗的影响，通过评估不同超参数初始化的多种设置在两种硬件配置上的实验结果，以及在基准结果之上进行的预训练和多任务训练实验证实它们对可持续机器学习的潜力。