Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
Takuya Narihira, Alonsogarcia, Javier, Cardinaux, Fabien, Akio Hayakawa, Masato Ishii, Iwaki, Kazunori, Kemp, Thomas, Yoshiyuki Kobayashi, Lukas Mauch, Akira Nakamura, Obuchi, Yukio, Andrew Shin, Suzuki, Kenji, Tiedmann, Stephen, Uhlich, Stefan, Takuya Yashima, Kazuki Yoshiyama · arXiv (Cornell University) · 2021
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.