Quantifying the Carbon Emissions of Machine Learning

TL;DR

This paper introduces a tool to estimate the carbon emissions from training machine learning models, highlighting key factors influencing environmental impact and suggesting mitigation strategies.

Contribution

It presents the Machine Learning Emissions Calculator, a novel tool for quantifying and understanding the environmental impact of ML training.

Findings

The calculator estimates emissions based on server location, hardware, and training duration.

It provides actionable recommendations for practitioners to reduce carbon footprint.

The study emphasizes the importance of considering environmental factors in ML development.

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.