Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
Machine Learning
2021-06-22 v2 Computer Vision and Pattern Recognition
Abstract
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools. In this paper, we introduce Neural Network Libraries (https://nnabla.org), a deep learning framework designed from engineer's perspective, with emphasis on usability and compatibility as its core design principles. We elaborate on each of our design principles and its merits, and validate our attempts via experiments.
Cite
@article{arxiv.2102.06725,
title = {Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives},
author = {Takuya Narihira and Javier Alonsogarcia and Fabien Cardinaux and Akio Hayakawa and Masato Ishii and Kazunori Iwaki and Thomas Kemp and Yoshiyuki Kobayashi and Lukas Mauch and Akira Nakamura and Yukio Obuchi and Andrew Shin and Kenji Suzuki and Stephen Tiedmann and Stefan Uhlich and Takuya Yashima and Kazuki Yoshiyama},
journal= {arXiv preprint arXiv:2102.06725},
year = {2021}
}
Comments
https://nnabla.org