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

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.

Automated summary

We introduce eco2AI, an open-source package for tracking the energy consumption and CO2 emissions of AI models in a straightforward way. Our focus is on accurately accounting for energy consumption and regional CO2 emissions. We encourage researchers to search for new AI architectures with lower computational costs, promoting both Sustainable AI and Green AI pathways.