  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.
