  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 presentEnergyVis, 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.
