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

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