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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
OriginalsprogEngelsk
TitelICML Workshop on "Challenges in Deploying and monitoring Machine Learning Systems"
Publikationsdato6 jul. 2020
StatusUdgivet - 6 jul. 2020
BegivenhedICML Workshop on "Challenges in Deploying and monitoring Machine Learning Systems" - Virtual
Varighed: 17 jul. 2020 → …
https://icml.cc/Conferences/2020/Schedule?showEvent=5738

Workshop

WorkshopICML Workshop on "Challenges in Deploying and monitoring Machine Learning Systems"
LokationVirtual
Periode17/07/2020 → …
Internetadresse
NavnarXiv

ID: 244567117