A comparative study of open source deep learning frameworks

• Published in: 2018 9th International Conference on Information and Communication Systems (ICICS) pp. 72 - 77
• Main Authors: Shatnawi, Ali, Al-Bdour, Ghadeer, Al-Qurran, Raffi, Al-Ayyoub, Mahmoud
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2018
• Subjects: C++ languages; CIFAR-10; CNN; CNTK; Deep Learning; Graphics processing units; Libraries; Machine learning; MNIST; Portable computers; Python; Servers; TensorFlow; Theano
• ISSN: 2573-3346
• DOI: 10.1109/IACS.2018.8355444

Deep Learning (DL) is one of the hottest trends in machine learning as DL approaches produced results superior to the state-of-the-art in problematic areas such as image processing and natural language processing (NLP). To foster the growth of the DL community, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three of the most popular and most comprehensive DL frameworks (namely Google's TensorFlow, University of Montreal's Theano, and Microsoft's CNTK). The ultimate goal of this work is to help end users make an informed decision about the best DL framework that suits their needs and resources. To ensure that our study is as comprehensive as possible, we conduct several experiments using multiple benchmark datasets and measure the performance of the frameworks' implementation of different DL algorithms. For most of our experiments, we find out that CNTK's implementations are superior to the other ones under consideration.