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Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools

Nesrine Bannour 1 Sahar Ghannay 1 Aurélie Névéol 1 Anne-Laure Ligozat 
1 ILES - Information, Langue Ecrite et Signée
LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, STL - Sciences et Technologies des Langues
Abstract : Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, costbenefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO 2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational setups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.
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Submitted on : Thursday, November 18, 2021 - 3:22:09 PM
Last modification on : Friday, August 5, 2022 - 9:27:31 AM
Long-term archiving on: : Saturday, February 19, 2022 - 7:25:20 PM


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  • HAL Id : hal-03435068, version 1


Nesrine Bannour, Sahar Ghannay, Aurélie Névéol, Anne-Laure Ligozat. Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools. EMNLP, Workshop SustaiNLP, Nov 2021, Punta Cana, Dominican Republic. ⟨hal-03435068⟩



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