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

Semen Budennyy, Vladimir Lazarev, Nikita Zakharenko, Alexey Korovin, Olga Plosskaya +7 more
7/31/2022
cs.LGcs.AIcs.CE

No official code found. Best implementation: sb-ai-lab/Eco2AI (277 stars, Apache-2.0, last commit 2025-03-10).

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.

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Code Implementations(1)

sb-ai-lab/Eco2AICommunity100%
Apache-2.0

eco2AI is a python library which accumulates statistics about power consumption and CO2 emission during running code.

27723PythonMay 31, 20221 years agoApache-2.0
aicarbon-emissionscarbon-footprintco2-emissionsco2-monitoring+10 more

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Cite this paper

@article{budennyy2022ecoai,
  title  = {Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI},
  author = {Semen Budennyy and Vladimir Lazarev and Nikita Zakharenko and Alexey Korovin and Olga Plosskaya and Denis Dimitrov and Vladimir Arkhipkin and Ivan Oseledets and Ivan Barsola and Ilya Egorov and Aleksandra Kosterina and Leonid Zhukov},
  year   = {2022},
  eprint = {2208.00406},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.00406v2}
}

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