首个可测量推荐系统能耗的工具，助力绿色AI研究。
EMERS: Energy Meter for Recommender Systems
- 用智能插座采集推荐系统实验能耗数据
- 提供界面实现能耗对比与记录
- 适合关注算法可持续性的研究者使用
随着机器学习的发展，推荐系统在训练、评估和部署阶段消耗的能量日益增加。然而，该领域的研究通常不报告实验的能耗数据。当前缺乏便捷测量推荐系统能耗的工具。为此，我们提出EMERS，首个简化测量、监控、记录和共享推荐系统实验能耗的软件库。EMERS通过智能插座测量能耗，并提供用户界面用于监控和比较不同实验的能耗。该工具提升了研究者对可持续性的认知，简化了自我报告能耗的过程，适用于推荐系统从业者与研究人员。
原文摘要 · Abstract (English)
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems community often does not report the energy consumption of their experiments. In today's research landscape, no tools exist to easily measure the energy consumption of recommender systems experiments. To bridge this gap, we introduce EMERS, the first software library that simplifies measuring, monitoring, recording, and sharing the energy consumption of recommender systems experiments. EMERS measures energy consumption with smart power plugs and offers a user interface to monitor and compare the energy consumption of recommender systems experiments. Thereby, EMERS improves sustainability awareness and simplifies self-reporting energy consumption for recommender systems practitioners and researchers.
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