***Notice: As [announced](https://chainer.org/announcement/2019/12/05/released-v7.html), Chainer is under the maintenance phase and further development will be limited to bug-fixes and maintenance only.***

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<div align="center"><img src="https://raw.githubusercontent.com/chainer/chainer/master/docs/image/chainer_red_h.png" width="400"/></div>

# Chainer: A deep learning framework

[![pypi](https://img.shields.io/pypi/v/chainer.svg)](https://pypi.python.org/pypi/chainer)
[![GitHub license](https://img.shields.io/github/license/chainer/chainer.svg)](https://github.com/chainer/chainer)
[![travis](https://img.shields.io/travis/chainer/chainer/master.svg)](https://travis-ci.org/chainer/chainer)
[![coveralls](https://img.shields.io/coveralls/chainer/chainer.svg)](https://coveralls.io/github/chainer/chainer)
[![Read the Docs](https://readthedocs.org/projects/chainer/badge/?version=stable)](https://docs.chainer.org/en/stable/?badge=stable)
[![Optuna](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org)

[**Website**](https://chainer.org/)
| [**Docs**](https://docs.chainer.org/en/stable/)
| [**Install Guide**](https://docs.chainer.org/en/stable/install.html)
| **Tutorials** ([ja](https://tutorials.chainer.org/ja/))
| **Examples** ([Official](examples), [External](https://github.com/chainer-community/awesome-chainer))
| [**Concepts**](https://docs.chainer.org/en/stable/guides/)
| [**ChainerX**](#chainerx)

**Forum** ([en](https://groups.google.com/forum/#!forum/chainer), [ja](https://groups.google.com/forum/#!forum/chainer-jp))
| **Slack invitation** ([en](https://bit.ly/go-chainer-slack), [ja](https://bit.ly/go-chainer-jp-slack))
| **Twitter** ([en](https://twitter.com/CuPy_Team), [ja](https://twitter.com/ChainerJP))

*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](https://github.com/cupy/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.

## Installation

*For more details, see the [installation guide](https://docs.chainer.org/en/stable/install.html).*

To install Chainer, use `pip`.

```sh
$ pip install chainer
```

To enable CUDA support, [CuPy](https://github.com/cupy/cupy) is required.
Refer to the [CuPy installation guide](https://docs-cupy.chainer.org/en/stable/install.html).


## Docker image

We are providing the official Docker image.
This image supports [nvidia-docker](https://github.com/NVIDIA/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

See the [contribution guide](https://docs.chainer.org/en/stable/contribution.html).


## ChainerX

See the [ChainerX documentation](https://docs.chainer.org/en/stable/chainerx/index.html).


## License

MIT License (see `LICENSE` file).


## More information

- [Release notes](https://github.com/chainer/chainer/releases)

## 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](https://dl.acm.org/citation.cfm?id=3330756) [BibTex](chainer2019_bibtex.txt)

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](http://learningsys.org/papers/LearningSys_2015_paper_33.pdf), [BibTex](chainer_bibtex.txt)

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](http://learningsys.org/nips17/assets/papers/paper_25.pdf), [BibTex](chainermn_bibtex.txt)