  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.
