# Chainer: A deep learning framework [](https://pypi.python.org/pypi/chainer) [](https://github.com/chainer/chainer) [](https://travis-ci.org/chainer/chainer) [](https://coveralls.io/github/chainer/chainer) [](https://docs.chainer.org/en/stable/?badge=stable) [**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](https://github.com/chainer/chainer/tree/master/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/join-chainer-slack), [ja](https://bit.ly/join-chainer-jp-slack)) | **Twitter** ([en](https://twitter.com/ChainerOfficial), [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. ## Stable version The stable version of current Chainer is separated in here: [v6](https://github.com/chainer/chainer/tree/v6). ## Installation To install Chainer, use `pip`. ```sh $ pip install chainer ``` To enable CUDA support, [set up CUDA](https://docs.nvidia.com/cuda/index.html#installation-guides) and install [CuPy](https://github.com/cupy/cupy). ```sh $ pip install cupy ``` [See the installation guide for more details](https://docs.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 Any contributions to Chainer are welcome! If you want to file an issue or send a pull request, [please follow 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) ## Reference 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)