Metadata-Version: 2.1
Name: chainer
Version: 7.7.0
Summary: A flexible framework of neural networks
Home-page: https://chainer.org/
Author: Seiya Tokui
Author-email: tokui@preferred.jp
License: MIT License
Description: <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.
        
        ***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.***
        
        ## 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)
        
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