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

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