Chainer: A deep learning framework
Website, Docs, Install Guide, Tutorials (ja), Examples (Official, External), Concepts, 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.
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
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 the ChainerX documentation.
License
MIT License (see LICENSE file).
More information
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