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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.
Notice: As
announced
, 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
.
To install Chainer, use
pip
.
$ pip install chainer
To enable CUDA support, CuPy is required.
Refer to the CuPy installation guide.
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
See the contribution guide.
ChainerX
See the ChainerX documentation.
License
MIT License (see LICENSE
file).
More information
Release notes
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 BibTex
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
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