Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.

The environmental sustainability of machine learning models is increasingly receiving attention, however mostly from academia. Here, the conversation tends to focus on the carbon footprint of language models which are not necessarily representative of the general machine learning field and not enough attention is paid to the operations phase of machine learning models. In addition, existing material on the topic of the environmental impact of machine learning puts too little emphasis on how the environmental impact can actually be estimated and reduced. This article is an attempt to address these issues and is written for practitioners and researchers alike who do hands-on machine learning. Although this post was written with machine learning in mind, some of the contents is also applicable to general software engineering. Before we begin, I want to emphasise that this post was not written to point fingers or moralise. The aim is simply to present information that you may or may not find relevant to your daily activities. All software -- from the apps that run on our phones to the data science pipelines that run in the cloud -- consume electricity and as long as not all our electricity is generated by renewable energy sources, electricity consumption will have a carbon footprint.

CodeCarbon, a lightweight, open-source software package that integrates into a Python codebase, is one of the tools that can help organizations conduct these steps. By automatically fetching power and grid data, CodeCarbon can track the amount of carbon dioxide (CO2) produced by the cloud or by local computing resources used to execute an experiment such as training a machine-learning algorithm. It then provides developers with dashboards displaying the CO2 outcomes of the experiment or series of experiments. This visibility into the CO2 impact creates opportunities to reduce the resulting carbon footprints, by hosting the cloud infrastructure in geographical regions that use renewable energy sources, or by using more efficient hardware. CodeCarbon was jointly developed by Mila, a world-leading AI research institute in Montreal; BCG GAMMA, Boston Consulting Group's global data science and AI team; Haverford College in Pennsylvania; and Comet, a meta machine-learning platform.