Chainer: A deep learning framework

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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.

Stable version

The stable version of current Chainer is separated in here: v6.

Installation

For more details, see the installation guide.

To install Chainer, use pip.

$ pip install chainer

To enable CUDA support, CuPy is required. Refer to the CuPy installation guide.

Docker image

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

Contribution

Any contributions to Chainer are welcome! If you want to file an issue or send a pull request, please follow the contribution guide.

ChainerX

See the ChainerX documentation.

License

MIT License (see LICENSE file).

More information

• Release notes

References

Tokui, Seiya, et al. "Chainer: A Deep Learning Framework for Accelerating the Research Cycle." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019. URL BibTex

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