Chainer: A Deep Learning Framework for Accelerating the Research Cycle
This framework addresses the need for efficient and user-friendly tools for researchers and practitioners in deep learning, though it is incremental as it builds upon existing software paradigms.
The authors introduced Chainer, a deep learning framework designed to accelerate the research cycle by offering flexibility, intuitiveness, and high performance for implementing a wide range of models, with features like GPU acceleration via CuPy, dynamic model support through Define-by-Run, and add-ons for computer vision and distributed training.
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.