| Name | chainer JSON |
| Version |
7.8.1
JSON |
| download |
| home_page | https://chainer.org/ |
| Summary | A flexible framework of neural networks |
| upload_time | 2022-01-05 05:33:36 |
| maintainer | |
| docs_url | None |
| author | Seiya Tokui |
| requires_python | >=3.5.0 |
| license | MIT License |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<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
[](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)
Raw data
{
"_id": null,
"home_page": "https://chainer.org/",
"name": "chainer",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.5.0",
"maintainer_email": "",
"keywords": "",
"author": "Seiya Tokui",
"author_email": "tokui@preferred.jp",
"download_url": "https://files.pythonhosted.org/packages/86/40/0d27458c1ac3e1c8f1a62ab62e3ec9e443412fa51d1e638ac669706097b7/chainer-7.8.1.tar.gz",
"platform": "",
"description": "<div align=\"center\"><img src=\"https://raw.githubusercontent.com/chainer/chainer/master/docs/image/chainer_red_h.png\" width=\"400\"/></div>\n\n# Chainer: A deep learning framework\n\n[](https://pypi.python.org/pypi/chainer)\n[](https://github.com/chainer/chainer)\n[](https://travis-ci.org/chainer/chainer)\n[](https://coveralls.io/github/chainer/chainer)\n[](https://docs.chainer.org/en/stable/?badge=stable)\n[](https://optuna.org)\n\n[**Website**](https://chainer.org/)\n| [**Docs**](https://docs.chainer.org/en/stable/)\n| [**Install Guide**](https://docs.chainer.org/en/stable/install.html)\n| **Tutorials** ([ja](https://tutorials.chainer.org/ja/))\n| **Examples** ([Official](examples), [External](https://github.com/chainer-community/awesome-chainer))\n| [**Concepts**](https://docs.chainer.org/en/stable/guides/)\n| [**ChainerX**](#chainerx)\n\n**Forum** ([en](https://groups.google.com/forum/#!forum/chainer), [ja](https://groups.google.com/forum/#!forum/chainer-jp))\n| **Slack invitation** ([en](https://bit.ly/go-chainer-slack), [ja](https://bit.ly/go-chainer-jp-slack))\n| **Twitter** ([en](https://twitter.com/CuPy_Team), [ja](https://twitter.com/ChainerJP))\n\n*Chainer* is a Python-based deep learning framework aiming at flexibility.\nIt 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.\nIt also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.\nFor more details about Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter.\n\n***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.***\n\n## Installation\n\n*For more details, see the [installation guide](https://docs.chainer.org/en/stable/install.html).*\n\nTo install Chainer, use `pip`.\n\n```sh\n$ pip install chainer\n```\n\nTo enable CUDA support, [CuPy](https://github.com/cupy/cupy) is required.\nRefer to the [CuPy installation guide](https://docs-cupy.chainer.org/en/stable/install.html).\n\n\n## Docker image\n\nWe are providing the official Docker image.\nThis image supports [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).\nLogin to the environment with the following command, and run the Python interpreter to use Chainer with CUDA and cuDNN support.\n\n```\n$ nvidia-docker run -it chainer/chainer /bin/bash\n```\n\n\n## Contribution\n\nSee the [contribution guide](https://docs.chainer.org/en/stable/contribution.html).\n\n\n## ChainerX\n\nSee the [ChainerX documentation](https://docs.chainer.org/en/stable/chainerx/index.html).\n\n\n## License\n\nMIT License (see `LICENSE` file).\n\n\n## More information\n\n- [Release notes](https://github.com/chainer/chainer/releases)\n\n## References\n\nTokui, 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.\n[URL](https://dl.acm.org/citation.cfm?id=3330756) [BibTex](chainer2019_bibtex.txt)\n\nTokui, S., Oono, K., Hido, S. and Clayton, J.,\nChainer: a Next-Generation Open Source Framework for Deep Learning,\n*Proceedings of Workshop on Machine Learning Systems(LearningSys) in\nThe Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS)*, (2015)\n[URL](http://learningsys.org/papers/LearningSys_2015_paper_33.pdf), [BibTex](chainer_bibtex.txt)\n\nAkiba, T., Fukuda, K. and Suzuki, S.,\nChainerMN: Scalable Distributed Deep Learning Framework,\n*Proceedings of Workshop on ML Systems in\nThe Thirty-first Annual Conference on Neural Information Processing Systems (NIPS)*, (2017)\n[URL](http://learningsys.org/nips17/assets/papers/paper_25.pdf), [BibTex](chainermn_bibtex.txt)\n\n\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "A flexible framework of neural networks",
"version": "7.8.1",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "759cac1eeadcfdcaa1a73e59dd981867",
"sha256": "179f06176f8d7a42385bc7811956db04e68213cfe30f5081491b9e2b6ff70de8"
},
"downloads": -1,
"filename": "chainer-7.8.1.tar.gz",
"has_sig": false,
"md5_digest": "759cac1eeadcfdcaa1a73e59dd981867",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.5.0",
"size": 1044012,
"upload_time": "2022-01-05T05:33:36",
"upload_time_iso_8601": "2022-01-05T05:33:36.127229Z",
"url": "https://files.pythonhosted.org/packages/86/40/0d27458c1ac3e1c8f1a62ab62e3ec9e443412fa51d1e638ac669706097b7/chainer-7.8.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-01-05 05:33:36",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "chainer"
}
