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

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