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A flexible framework of neural networks for deep learning
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Chainer: A deep learning framework

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Website | Docs | Install Guide | Tutorials (ja) | Examples (Official, External) | Concepts | ChainerX

Forum (en, ja) | Slack invitation (en, ja) | Twitter (en, ja)

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.


To install Chainer, use pip.

$ pip install chainer

To enable CUDA support, set up CUDA and install CuPy.

$ pip install cupy

See the installation guide for more details.

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


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


See the ChainerX documentation.


MIT License (see LICENSE file).

More information


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

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