A flexible framework of neural networks
Project description
Chainer: A deep learning framework
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
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