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.
@article{arxiv.2409.15060,
title = {EMERS: Energy Meter for Recommender Systems},
author = {Lukas Wegmeth and Tobias Vente and Alan Said and Joeran Beel},
journal= {arXiv preprint arXiv:2409.15060},
year = {2024}
}
Comments
Accepted at the RecSoGood 2024 Workshop co-located with the 18th ACM Conference on Recommender Systems