Source

Doklady Mathematics

DATE OF PUBLICATION

01/19/2023

Authors

Leonid Zhukov Semen Budennyy

Alexey Korovin

Vladimir Lazarev

Ivan Oseledets

Denis Dimitrov

Ivan Barsola I.V. Egorov A. A. Kosterina I.V. Pavlov V. S. Akhripkin Nikita Zakharenko O.A. Plosskaya

eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI

ESG, Sustainability, Carbon footprint, Ecology, CO2 emissions, GHG

Abstract

The size and complexity of deep neural networks used in AI applications 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 the energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we focus on accurate tracking of energy consumption and regional CO2 emissions accounting. We encourage the research for community to search for new optimal Artificial Intelligence (AI) architectures with lower computational cost. The motivation also comes from the concept of AI-based greenhouse gases sequestrating cycle with both Sustainable AI and Green AI pathways. The code and documentation are hosted on Github under the Apache 2.0 license https://github.com/sb-ai-lab/Eco2AI.

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