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Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details about Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter.
The stable version of current Chainer is separated in here: v5.
To install Chainer, use pip.
$ pip install chainerTo enable CUDA support, set up CUDA and install CuPy.
$ pip install cupySee the installation guide for more details.
We are providing the official Docker image. This image supports nvidia-docker. Login to the environment with the following command, and run the Python interpreter to use Chainer with CUDA and cuDNN support.
$ nvidia-docker run -it chainer/chainer /bin/bash
Any contributions to Chainer are welcome! If you want to file an issue or send a pull request, please follow the contribution guide.
See this separate user guide for how to build and run Chainer with ChainerX.
MIT License (see LICENSE file).
Tokui, S., Oono, K., Hido, S. and Clayton, J., Chainer: a Next-Generation Open Source Framework for Deep Learning, Proceedings of Workshop on Machine Learning Systems(LearningSys) in The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), (2015) URL, BibTex
Akiba, T., Fukuda, K. and Suzuki, S., ChainerMN: Scalable Distributed Deep Learning Framework, Proceedings of Workshop on ML Systems in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), (2017) URL, BibTex