Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
This addresses the environmental impact of deep learning for practitioners and researchers, but it is incremental as it builds on existing monitoring concepts without introducing a new paradigm.
The authors tackled the problem of the high energy consumption and carbon footprint of training deep learning models by developing Carbontracker, a tool for tracking and predicting these environmental impacts, with the result that it enables practitioners to monitor and potentially reduce emissions, though no concrete numbers are provided in the 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.