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.

本研究旨在通过研究绿色机器学习，考察不同模型架构和超参数在训练和推理阶段中的能效实践，以提供可持续的机器学习操作的实用指南，强调降低能耗和碳足迹同时保持性能。

ML 训练与推断中的能耗实证研究

Computing Within Limits: An Empirical Study of Energy Consumption in ML  Training and Inference

Fueled by the soaring popularity of large language and foundation models, the
accelerated growth of artificial intelligence (AI) models' enormous
environmental footprint has come under increased scrutiny. While many
approaches have been proposed to make AI more energy-efficient and
environmentally friendly, environmental inequity -- the fact that AI's
environmental footprint can be disproportionately higher in certain regions
than in others -- has emerged, raising social-ecological justice concerns. This
paper takes a first step toward addressing AI's environmental inequity by
balancing its regional negative environmental impact. Concretely, we focus on
the carbon and water footprints of AI model inference and propose equity-aware
geographical load balancing (GLB) to explicitly address AI's environmental
impacts on the most disadvantaged regions. We run trace-based simulations by
considering a set of 10 geographically-distributed data centers that serve
inference requests for a large language AI model. The results demonstrate that
existing GLB approaches may amplify environmental inequity while our proposed
equity-aware GLB can significantly reduce the regional disparity in terms of
carbon and water footprints.

本研究提出了一种地域负担均衡的方案以解决人工智能带来的环境问题，并通过基于管辖区域的碳和水足迹进行模拟，证明该方案可以显著减少碳和水足迹造成的环境不平等问题。