Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI
This addresses the environmental impact of AI for researchers and data scientists, though it is incremental as it builds on existing tracking tools.
The authors tackled the problem of growing energy consumption and CO2 emissions from training and using deep neural networks by introducing eco2AI, an open-source package that tracks energy use and equivalent CO2 emissions for machine learning models, aiming to encourage the development of more efficient AI architectures.
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.