Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

TLDR

Accurate reporting of energy and carbon usage is essential for understanding the climate impacts of machine learning research. The authors present a framework that offers a simple interface for real‑time tracking of energy consumption and carbon emissions, generates standardized online appendices, and uses this framework to create a leaderboard that incentivizes energy‑efficient reinforcement learning algorithms while encouraging broader sustainable ML research. The framework provides a user‑friendly interface for monitoring real‑time energy use and carbon emissions and automatically produces standardized online appendices. Using the framework, the authors established a leaderboard for energy‑efficient reinforcement learning algorithms and, from case studies, proposed strategies to mitigate carbon emissions and reduce energy consumption.

Abstract

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.