  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.
