Performance analysis of machine learning libraries
Abstract
The paper presents results of performance analysis of machine learning libraries. The research was based on ML.NET and TensorFlow tools. The analysis was based on a comparison of running time of the libraries, during detection of objects on sets of images, using hardware with different parameters. The library, consuming fewer hardware resources, turned out to be TensorFlow. The choice of hardware platform and the possibility of using graphic cores, affecting the increase in computational efficiency, turned out to be not without significance.
Keywords:
machine learning, performance, ML.NET, TensorFlowReferences
Leading business in the age of AI, https://news.microsoft.com/europe/features/leaders-look-to-embrace-ai-and-high-growth-companies-are-seeing-the-benefits, [29.11.2020].
Google Scholar
Przewodnik po strukturze ML.NET, https://docs.microsoft.com/pl-pl/dotnet/machine-learning/how-does-mldotnet-work, [30.11.2020].
Google Scholar
M. N. Gevorkyan, A. V. Demidova, T. S. Demidova, A. Sobolev, Review and comparative analysis of machine learning libraries for machine learning, Discrete And Continuous Models And Applied Computational Science 27 (2019) 305-315, http://dx.doi.org/10.22363/2658-4670-2019-27-4-305-315.
DOI: https://doi.org/10.22363/2658-4670-2019-27-4-305-315
Google Scholar
A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly, 2019.
Google Scholar
E. Brill, M. Banko, Scaling to very large corpora for natural language disambiguation, Proceedings of 39th Annual Meeting on Association for Computational Linguistics (2001) 26-33, https://doi.org/10.3115/1073012.1073017.
DOI: https://doi.org/10.3115/1073012.1073017
Google Scholar
V. Shankar, R. Roelofs, H. Mania, A. Fang, B. Recht, L. Schmidt, Evaluating Machine Accuracy on ImageNet, 37th International Conference on Machine Learning (2020) 8634-8644.
Google Scholar
E. Zuccarelli, Using machine learning to predict car accidents, https://towardsdatascience.com/using-machine-learning-to-predict-car-accidents-44664c79c942, [15.06.2021].
Google Scholar
M. Hartley, T. S. G. Olsson, dtoolAI: Reproducibility for Deep Learning, Patterns 1(5) (2020) 100099, https://doi.org/10.1016/j.patter.2020.100073.
DOI: https://doi.org/10.1016/j.patter.2020.100073
Google Scholar
C. Deng, X. Ji, C. Rainey, J. Zhang, W. Lu, Integrating Machine Learning with Human Knowledge, iScience 23(11) (2020) 101656, https://doi.org/10.1016/j.isci.2020.101656.
DOI: https://doi.org/10.1016/j.isci.2020.101656
Google Scholar
Optimize TensorFlow performance using the Profiler, https://www.tensorflow.org/guide/profiler, [15.06.2021].
Google Scholar
G. Nguyen, S. Dlugolinsky, M. Bobák, V. Tran, Á. García, I. Heredia, P. Malík, L. Hluchý, Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey, Artificial Intelligence Review 52 (2019) 77-124, https://doi.org/10.1007/s10462-018-09679-z.
DOI: https://doi.org/10.1007/s10462-018-09679-z
Google Scholar
F. Florencio, E. D. M. Ordonez, T. V. Silva, M. C. Júnior, Performance Analysis of Deep Learning Libraries: TensorFlow and PyTorch, Journal of Computer Science 15 (2019) 785-799, http://dx.doi.org/10.3844/jcssp.2019.785.799.
DOI: https://doi.org/10.3844/jcssp.2019.785.799
Google Scholar
Z. Ahmed, S. Amizadeh, M. Bilenko, R. Carr, W.-S. Chin, Y. Dekel, X. Dupre, V. Eksarevskiy, E. Erhardt, C. Eseanu, S. Filipi, T. Finley, A. Goswami, M. Hoover, S. Inglis, M. Interlandi, S. Katzenber, Machine Learning at Microsoft with ML.NET, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019) 2448-2458, https://doi.org/10.1145/3292500.3330667.
DOI: https://doi.org/10.1145/3292500.3330667
Google Scholar
T. Jin, G.-T. Bercea, T. D. Le, T. Chen, G. Su, H. Imai, Y. Negishi, A. Leu, K. O'Brien, K. Kawachiya, A. E. Eichenberger, Compiling ONNX Neural Network Models Using MLIR (2020), https://arxiv.org/abs/2008.08272.
Google Scholar
J. Redmon, YOLO: Real-Time Object Detection, https://pjreddie.com/darknet/yolov2, [10.06.2021].
Google Scholar
J. Redmon, A. Farhadi, YOLO9000:Better, Faster, Stronger (2016), https://arxiv.org/abs/1612.08242.
DOI: https://doi.org/10.1109/CVPR.2017.690
Google Scholar
W. Fang, L. Wang, P. Ren, Tinier-YOLO: A Real-Time Object Detection, IEEE Access 8 (2019) 1935-1944, https://doi.org/10.1109/ACCESS.2019.2961959.
DOI: https://doi.org/10.1109/ACCESS.2019.2961959
Google Scholar
Authors
Ewa Justyna KędzioraPoland
Statistics
Abstract views: 259PDF downloads: 213
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.