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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.
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.
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
See the contribution guide.
See the ChainerX documentation.
MIT License (see LICENSE file).
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
FAQs
A flexible framework of neural networks
We found that chainer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 8 open source maintainers collaborating on the project.
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