Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2020
Abstract
Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory study on the dependency networks of deep learning libraries, namely, Tensorflow, PyTorch, and Theano. We study the project purposes, application domains, dependency degrees, update behaviors and reasons as well as version distributions of deep learning projects that depend on Tensorflow, PyTorch, and Theano. Our study unveils some commonalities in various aspects (e.g., purposes, application domains, dependency degrees) of deep learning libraries and reveals some discrepancies as for the update behaviors, update reasons, and the version distributions. Our findings highlight some directions for researchers and also provide suggestions for deep learning developers and users.
Keywords
Deep learning frameworks, deep learning platforms, deep learning deployment, empirical study
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2020 36th IEEE International Conference on Software Maintenance and Evolution (ICSME): Adelaide, September 27 - October 3: Proceedings
First Page
868
Last Page
878
ISBN
9781728156194
Identifier
10.1109/ICSME46990.2020.00116
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
HAN, Junxiao; DENG, Shuiguang; LO, David; ZHI, Chen; YIN, Jianwei; and XIA, Xin.
An empirical study of the dependency networks of deep learning libraries. (2020). 2020 36th IEEE International Conference on Software Maintenance and Evolution (ICSME): Adelaide, September 27 - October 3: Proceedings. 868-878.
Available at: https://ink.library.smu.edu.sg/sis_research/5626
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICSME46990.2020.00116