Progress in Machine Learning (ML) in recent years has been meteorical, with major breakthroughs happening in domains such as machine translation, image recognition and generation. While these advances are having widespread applications in many domains, detecting cancers on X-rays and improving the prediction of supply and demand,the carbon impact of ML training has not been a central part of the conversation until recently. Therefore, as ML practitioners who are also aware of the overall state of the environment, we find that it is important to develop this conversion further and work towards building the tools we need to assess the carbon emissions generated by the models we train, as well as ways to reduce those emissions. We have published this short paper to the Climate Change AI workshop at NeurIPS 2019. The sources are available in the csv file above.