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arXiv:1910.09700cs.CY

Quantifying the Carbon Emissions of Machine Learning

Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres

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Abstract

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.

arXiv comments
Machine Learning Emissions Calculator: https://mlco2.github.io/impact/
Journal reference
DOI
Subjects
cs.CYComputers and Society cs.LGMachine Learning

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No comments linking this paper were found in the collected sources. Found in 2 HN submissions from Jan 2022 to Mar 2022. Open submission discussions.

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No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

  • Ask HN: What is your take on carbon emissions of Machine Learning11 points10 comments

    At the latest since the widely cited article on Green AI [1], there has been increased discussion about the energy consumption of ML. Whenever I talk to colleagues about this, we always agree that there is a dangerous potential lying dormant here, but somehow it cannot be properly grasped. There is simply a lack of really good data. Calculators for emission like from Victor Schmidt and the whole Yoshua Bengio Team at Mila [2] [3] are very good tools for your own use, but can tell you little…

  • Ask HN: What is your take on carbon emissions of Machine Learning2 points0 comments

    At the latest since the widely cited article on Green AI [1], there has been increased discussion about the energy consumption of ML. Whenever I talk to colleagues about this, we always agree that there is a dangerous potential lying dormant here, but somehow it cannot be properly grasped. There is simply a lack of really good data. Calculators for emission like from Victor Schmidt and the whole Yoshua Bengio Team at Mila [2] [3] are very good tools for your own use, but can tell you little…

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