How to estimate carbon footprint when training deep learning models? A guide and review

التفاصيل البيبلوغرافية
العنوان: How to estimate carbon footprint when training deep learning models? A guide and review
المؤلفون: Heguerte, Lucia Bouza, Bugeau, Aurélie, Lannelongue, Loïc
سنة النشر: 2023
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
الوصف: 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.
Comment: Environmental Research Communications, 2023
نوع الوثيقة: Working Paper
DOI: 10.1088/2515-7620/acf81b
URL الوصول: http://arxiv.org/abs/2306.08323
رقم الأكسشن: edsarx.2306.08323
قاعدة البيانات: arXiv
الوصف
DOI:10.1088/2515-7620/acf81b