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arXiv · 1908.00213

Chainer: A Deep Learning Framework for Accelerating the Research Cycle

Seiya Tokui·Ryosuke Okuta·Takuya Akiba·Yusuke Niitani·Toru Ogawa·Shunta Saito·Shuji Suzuki·Kota Uenishi·Brian Vogel·Hiroyuki Yamazaki Vincent
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Abstract

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.

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Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent. 2019-08-01. Chainer: A Deep Learning Framework for Accelerating the Research Cycle. https://arxiv.org/abs/1908.00213

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