Some years ago, when I first joined the world of AI, I was super interested in measuring the environmental impact of the projects I was developing in my team. I recall being a regular user of CodeCarbon, an open-source French library which can be easily embedded into your code to measure the energy consumption and carbon emission of your machine learning training. You start a tracker, you run your training loop, you stop the tracker, and you get the kWh and CO2e emission of your experiment. I had embedded it into all my projects. Measuring not only the performance but also the emissions of models felt like a concrete step we could take to make a difference.

Two weeks ago, discussing some aspects of this tool with a colleague, I realized with some embarrassment I could not give an answer to some of their questions.…