论文标题
CarbonTracker：跟踪和预测训练深度学习模型的碳足迹
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
论文作者
论文摘要
深度学习（DL）可以在各种任务中取得令人印象深刻的成果，但这通常是以在专业硬件加速器上进行的大量培训模型的成本来实现的。近年来，这种能源密集型工作量的增长巨大。如果这种指数趋势继续下去，机器学习（ML）可能会成为气候变化的重要贡献。如果从业者知道自己的能量和碳足迹，那么他们可能会在可能的情况下积极采取措施减少它。在这项工作中，我们提出了CarbonTracker，这是一种用于跟踪和预测训练DL模型的能量和碳足迹的工具。我们提出，使用CarbonTracker等工具，报告了模型开发和培训的能量和碳足迹。我们希望这将促进ML中负责任的计算，并鼓励对节能深度神经网络的研究。
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.
