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

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 set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.

Measuring the Carbon Intensity of AI in Cloud Instances

“…Our tool builds upon this work by making carbon tracking on cloud instances possible, enabling a larger portion of ML model training work to profit from fine-grained carbon estimation. However, recent work has found that their results vary significantly and are not fully representative of the true emissions incurred by training [3]. Perhaps most similar to our work, EnergyVis [41] is an interactive tool for visualizing and comparing energy consumption of ML models as a function of hardware and physical location (U.S. state), given metadata about a model's energy use per epoch.…”

“…For a given datacenter, this can be turned into a factor which can be multiplied against the electricity consumption of computing equipment to get an estimate of the total consumption. Some companies have highlighted particularly low PUEs, such as Google claiming a PUE of 1.10 across its fleet of data centers for the 12 months ending in Q1 2021, 3 compared to an average global PUE of 1.59 [2].…”

“…We note that our conclusions drawn from experiments and analyses on time-shifting and location-shifting are still applicable with tools that measure more electricity than just the GPU 3. https://www.google.com/about/datacenters/efficiency/4 One of the largest single source of CO 2 emissions, contributing to 7%-8% of global emissions, is the production of cement[20].…”

Digital Information Literacy Skills among Library and Information Science Professionals in University Libraries of Sindh Pakistan

“…Nuclear power, hydro, and solar power generation sources yield least amount of carbon intensity while the oil, coal, and natural gas result in high-end of carbon intensity. Over the past years, some studies have carried out analysis on the carbon footprint of LLMs [17]- [21]. For instance, Bannour et al [21] performed carbon footprint analysis relative to LLMs using six different tools.…”

“…Over the past years, some studies have carried out analysis on the carbon footprint of LLMs [17]- [21]. For instance, Bannour et al [21] performed carbon footprint analysis relative to LLMs using six different tools. One of the tool is publicly available that computes the carbon footprint of LLMs [22].…”