Counting 3,663 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Author
Project Page
https://chainer.org/
Last Commit
Mar. 22, 2019
Created
Jun. 5, 2015

Chainer: A deep learning framework

pypi GitHub license travis coveralls Read the Docs

Website | Docs | Install Guide | Tutorial | Examples (Official, External) | ChainerX

Forum (en, ja) | Slack invitation (en, ja) | Twitter (en, ja)

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 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: v5.

Installation

To install Chainer, use pip.

$ pip install chainer

To enable CUDA support, set up CUDA and install CuPy.

$ pip install cupy

See the installation guide for more details.

Docker image

We are providing the official Docker image. This image supports 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.

ChainerX

See this separate user guide for how to build and run Chainer with ChainerX.

License

MIT License (see LICENSE file).

More information

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, BibTex

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, BibTex

Latest Releases
v6.0.0b3
 Feb. 27 2019
v5.3.0
 Feb. 27 2019
v6.0.0b2
 Jan. 24 2019
v5.2.0
 Jan. 24 2019
v5.1.0
 Dec. 2 2018