codecarbon

Great writeup! I've done some work on Codecarbon [1] in the past, it's a pretty standard tool for measuring ML/AI workload emissions, it would be awesome to have your findings contributed to that project as well. [1] https://github.com/mlco2/codecarbon

Good point, here's an estimator, have at it: https://github.com/mlco2/codecarbon

The underlying calculations apparently depend on the following CSV: https://github.com/mlco2/codecarbon/blob/master/codecarbon/d... Two problems I see here 1. They are using AWS, which is not in the data set 2. The calculation set seems questionable to me. The GCP Iowa data center has some of the best renewable usage, are they using the data center total (a larger data center with more renewable can still have more total emmissions vs only 8% of the compute I use should be included in the calculation. Either way, the underlying data for the calculation is out of date edit... I opened an issue with the codecarbon project to see if we can get more clarity on things. I've include links there for the breadcrumbs I followed https://github.com/mlco2/codecarbon/issues/483

Maybe helpful: https://github.com/mlco2/codecarbon Also: https://github.com/hubblo-org/scaphandre

Hi HN! Excited to share with you our recent open source project CodeCarbon - a collaboration between Mila, BCG GAMMA, Haverford College, and Comet.ml. CodeCarbon records the amount of power being used by the underlying infrastructure from major cloud providers and privately hosted on-premises data centers. Based on publicly available data sources, it estimates the amount of CO₂ emissions produced by referring to the carbon intensity from the energy mix of the electric grid to which the hardware is connected. Would love to hear your thoughts! Github: https://github.com/mlco2/codecarbon Blog post: https://www.comet.ml/site/introducing-codecarbon-an-open-sou... Demo (hosted on Comet.ml): https://www.comet.ml/mlco2/codecarbon-test/view/XFNDEoLvxysp...