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.

翻译：机器学习和深度学习模型已成为近年来人工智能在社会各行业快速发展的关键。目前普遍认识到，这些模型的开发会产生环境成本，这一点已在多项研究中得到分析。已有多种在线工具和软件被开发，用于追踪训练机器学习模型时的能源消耗。本文针对希望开始评估自身工作环境影响的人工智能实践者，提供了对这些工具的全面介绍与比较。我们回顾了相关专业术语以及每种工具的技术要求，并在两种用于图像处理的深度神经网络以及不同类型的服务器上，比较了各工具估算的能源消耗。基于这些实验，我们为更好地选择合适工具和基础设施提供了一些建议。