EMERS: Energy Meter for Recommender Systems

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...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Wegmeth, Lukas, Vente, Tobias, Said, Alan, Beel, Joeran
Format: Article
Language:English
Subjects:
Electrical plugs
Energy consumption
Machine learning
Recommender systems
Software
Online Access:Get full text
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Summary: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.
ISSN:2331-8422