Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investigates Green ML, examining various model architectures and hyperparameters in both training and inference phases to identify energy-efficient practices. Our study leverages software-based power measurements for ease of replication across diverse configurations, models and datasets. In this paper, we examine multiple models and hardware configurations to identify correlations across the various measurements and metrics and key contributors to energy reduction. Our analysis offers practical guidelines for constructing sustainable ML operations, emphasising energy consumption and carbon footprint reductions while maintaining performance. As identified, short-lived profiling can quantify the long-term expected energy consumption. Moreover, model parameters can also be used to accurately estimate the expected total energy without the need for extensive experimentation.

翻译：机器学习（ML）已取得巨大进展，但其环境足迹仍令人担忧。认识到ML日益增长的环境影响，本文研究了绿色ML，通过考察训练和推理阶段的各种模型架构与超参数，以识别节能实践。我们的研究利用基于软件的功耗测量，便于在不同配置、模型和数据集间复现。本文通过检验多种模型与硬件配置，识别了各类测量指标间的相关性以及能耗降低的关键因素。我们的分析为构建可持续的ML运维提供了实用指南，强调在保持性能的同时降低能耗与碳足迹。研究表明，短期性能分析可量化长期预期能耗。此外，模型参数亦可用于准确估算预期总能耗，无需进行大量实验。