  Software frameworks for neural networks play a key role in the development
and application of deep learning methods. In this paper, we introduce the
Chainer framework, which intends to provide a flexible, intuitive, and high
performance means of implementing the full range of deep learning models needed
by researchers and practitioners. Chainer provides acceleration using Graphics
Processing Units with a familiar NumPy-like API through CuPy, supports general
and dynamic models in Python through Define-by-Run, and also provides add-on
packages for state-of-the-art computer vision models as well as distributed
training.
