EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
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DOI：
10.1145/3411763.3451780
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Omar Shaikh;Jon Saad-Falcon;Austin P. Wright;Nilaksh Das;Scott Freitas;O. Asensio;Duen Horng Chau
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文献类型：
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作者：
Omar Shaikh;Jon Saad-Falcon;Austin P. Wright;Nilaksh Das;Scott Freitas;O. Asensio;Duen Horng Chau
大型机器学习（ML）模型的出现提高了从计算机视觉到自然语言等各种建模任务的最新（SOTA）性能。随着ML模型的规模不断增加，它们各自的能耗和计算需求也在增加。然而，跟踪、报告和比较能源消耗的方法仍然有限。我们介绍了EnergyVis，一个用于ML模型的交互式能耗跟踪器。EnergyVis由多个协调视图组成，使研究人员能够交互式地跟踪，可视化和比较关键能耗和碳足迹指标（千瓦时和二氧化碳）的模型能耗，帮助用户探索可能减少碳足迹的替代部署位置和硬件。EnergyVis旨在通过在模型训练期间交互式地突出过度的能源使用，以及通过提供替代训练选项来减少能源使用，来提高人们对计算可持续性的认识。
The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As ML models continue increasing in size, so does their respective energy consumption and computational requirements. However, the methods for tracking, reporting, and comparing energy consumption remain limited. We present EnergyVis, an interactive energy consumption tracker for ML models. Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training; and by providing alternative training options to reduce energy usage.