arXiv 2007.030512020-07-06cs.CYcs.LGeess.SPstat.ML

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

University of Copenhagen（哥本哈根大学）

Lasse F. Wolff Anthony, Benjamin Kanding, Raghavendra Selvan

备注：: 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/

摘要：

深度学习（DL）可以在各种任务中取得令人印象深刻的结果，但这通常是以在专用硬件加速器上长时间训练模型为代价的。近年来，这种能源密集型工作量大幅增长。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者。如果从业者意识到他们的能源和碳足迹，那么他们可能会积极采取措施，尽可能减少它。在这项工作中，我们提出了Carbontracker，用于跟踪和预测训练DL模型的能源和碳足迹的工具。我们建议使用Carbontracker等工具报告模型开发和培训的能源和碳足迹以及性能指标。我们希望这将促进ML中的负责任计算，并鼓励对节能深度神经网络的研究。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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