%global _empty_manifest_terminate_build 0
Name: python-chainer
Version: 7.8.1
Release: 1
Summary: A flexible framework of neural networks
License: MIT License
URL: https://chainer.org/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/86/40/0d27458c1ac3e1c8f1a62ab62e3ec9e443412fa51d1e638ac669706097b7/chainer-7.8.1.tar.gz
BuildArch: noarch
%description
# 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)
[](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)
%package -n python3-chainer
Summary: A flexible framework of neural networks
Provides: python-chainer
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-chainer
# 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)
[](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)
%package help
Summary: Development documents and examples for chainer
Provides: python3-chainer-doc
%description help
# 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)
[](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)
%prep
%autosetup -n chainer-7.8.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-chainer -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot - 7.8.1-1
- Package Spec generated
