Quantifying the Carbon Emissions of Machine Learning

• DOI: 10.48550/arxiv.1910.09700
• Publication Date: 2019-01-01
• Authors: Lacoste, Alexandre; Luccioni, Alexandra; Schmidt, Victor; Dandres, Thomas
• Keywords: FOS: Computer and information sciences; Computer Science - Computers and Society; Computer Science - Machine Learning; 13. Climate action; Computers and Society (cs.CY); 01 natural sciences; 0105 earth and related environmental sciences; Machine Learning (cs.LG)

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.<br/>Machine Learning Emissions Calculator: https://mlco2.github.io/impact/<br/>