Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

TL;DR

Carbontracker is a tool designed to monitor and forecast the energy consumption and carbon emissions of deep learning model training, aiming to promote environmentally responsible AI development.

Contribution

This paper introduces Carbontracker, a novel tool for tracking and predicting the energy and carbon footprint of deep learning training processes.

Findings

Carbontracker accurately estimates energy and carbon emissions during training.

Reporting energy and carbon metrics alongside performance metrics encourages responsible AI practices.

The tool promotes research into energy-efficient neural network architectures.

Abstract

Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen immense growth in recent years. Machine learning (ML) may become a significant contributor to climate change if this exponential trend continues. If practitioners are aware of their energy and carbon footprint, then they may actively take steps to reduce it whenever possible. In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models. We propose that energy and carbon footprint of model development and training is reported alongside performance metrics using tools like Carbontracker. We hope this will promote responsible computing in ML and encourage research into energy-efficient deep neural networks.