The text discusses the environmental impact of training large machine learning models, highlighting a study that estimated the significant carbon emissions produced by such processes. With the rise of bigger models like GPT-3, the energy consumption and associated emissions are increasing, prompting the need for transparency in the emissions associated with machine learning. In response, the open-source tool CodeCarbon was developed through a collaboration between Comet and leading AI researchers, providing a means for researchers to track and visualize the carbon footprint of their experiments. By integrating with platforms like Comet, CodeCarbon offers a user-friendly dashboard to present emissions data in relatable terms, encouraging the AI community to consider emissions as a critical performance metric and to incorporate these insights into research publications. The text underscores the importance of developing tools and practices that prioritize sustainability in AI research, urging the community to adopt new paradigms that address the environmental impact of their work.