Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI

TL;DR

Eco2AI is an open-source tool designed to accurately track energy use and CO2 emissions of machine learning models, promoting sustainable AI development and regional emissions accounting.

Contribution

It introduces eco2AI, the first tool focused on precise energy and CO2 emissions tracking for AI models, supporting research on low-cost architectures.

Findings

eco2AI enables accurate energy consumption measurement

It facilitates regional CO2 emissions accounting

Supports research on energy-efficient AI architectures

Abstract

The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. We encourage research community to search for new optimal Artificial Intelligence (AI) architectures with a lower computational cost. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.