Abstract
Although machine learning (ML) is a field that has been the subject of research for decades, a large number of applications with high computational power have recently emerged. Usually, we only focus on solving machine learning problems without considering how much energy has been consumed by the different frameworks used for such applications. This study aims to provide a comparison among four widely used frameworks such as Tensorflow, Keras, Pytorch, and Scikit-learn in terms of many aspects, including energy efficiency, memory usage, execution time, and accuracy. We monitor the performance of such frameworks using different well-known machine learning benchmark problems. Our results show interesting findings, such as slower and faster frameworks consuming less or more energy, higher or lower memory usage, etc. We show how to use our results to provide machine learning developers with information to decide which framework to use for their applications when energy efficiency is a concern.
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Acknowledgement
We want to thank the Ministry of Higher Education and Gabes University for facilitating the travel of Salwa Ajel to Portugal, the HASLab/INESC TEC, Universidade do Minho (Portugal) for the technical support of the work, and the Erasmus Jamies for accepting Salwa Ajel’s application.
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Ajel, S., Ribeiro, F., Ejbali, R., Saraiva, J. (2023). Energy Efficiency of Python Machine Learning Frameworks. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_57
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DOI: https://doi.org/10.1007/978-3-031-35507-3_57
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