  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.
