Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Research output: Contribution to conference › Paper › Research › peer-review
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Carbontracker : Tracking and Predicting the Carbon Footprint of Training Deep Learning Models. / Anthony, Lasse F. Wolff; Kanding, Benjamin; Selvan, Raghavendra.
2020. Paper presented at ICML Workshop on "Challenges in Deploying and monitoring Machine Learning Systems".Research output: Contribution to conference › Paper › Research › peer-review
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TY - CONF
T1 - Carbontracker
T2 - ICML Workshop on "Challenges in Deploying and monitoring Machine Learning Systems"
AU - Anthony, Lasse F. Wolff
AU - Kanding, Benjamin
AU - Selvan, Raghavendra
N1 - Accepted to be presented at the ICML Workshop on "Challenges in Deploying and monitoring Machine Learning Systems", 2020. Source code at this link https://github.com/lfwa/carbontracker/
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - cs.CY
KW - cs.LG
KW - eess.SP
KW - stat.ML
M3 - Paper
Y2 - 17 July 2020
ER -
ID: 255786102