Assessing Energy Efficiency of Machine Learning

June 9, 2026 · View on GitHub

Code and results for assesing energy efficiency of various ImageNet models, with an associated research paper published at ECML PKDD 2022.

Big News

Please also check out our open access follow-up work on Sustainable and Trustworthy Reporting (STREP). It is accompanied by a public website (extension of the original ELEx tool) that allows to investigate our experimental results without running any code locally! While this code base will not receive any further updates, feel free to look into the STREP software library.

Installation

All code was executed with Python 3.8, please refer to requirements for all dependencies. Depending on how you intend to use this software, only some packages are required.

Usage

To start ELEx locally, simply call python elex.py and open the given URL in any webbrowser. Call python -m mlee.label_generator to generate an energy label either for a given task / model / environment, or any of the merged logs (provided via command line). The results (plots and tables) in the paper were generated with the corresponding script.

New experiments can also be executed, available tasks are inference and training. You can pass the chosen model, software backend and more configuration options via command line. For --data-path pass the directory with full ImageNet data for the chose software --backend, refer to the respective implementations for TensorFlow and PyTorch. For each experiment a folder is created, which can be merged into more compact .json format. Note that due to monitoring of power draw, we mainly tested on limited hardware architectures and systems (Linux systems with Intel CPUs and NVIDIA GPUs). You can also inspect the scripts used to run all esxperiments.

Reference & Term of Use

Please refer to the license for terms of use. If you appreciate our work and code, please cite our paper as given by Springer:

Fischer, R., Jakobs, M., Mücke, S., Morik, K. (2023). A Unified Framework for Assessing Energy Efficiency of Machine Learning. In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_3

or using the bibkey below:

@inproceedings{fischer_unified_2022,
	location = {Cham},
	title = {A Unified Framework for Assessing Energy Efficiency of Machine Learning},
	rights = {All rights reserved},
	doi = {10.1007/978-3-031-23618-1_3},
	pages = {39--54},
	booktitle = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
	publisher = {Springer Nature Switzerland},
	author = {Fischer, Raphael and Jakobs, Matthias and Mücke, Sascha and Morik, Katharina},
	date = {2022},
}