DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.

Contact: webadmin@linux.intel.com

Intel® Optimization for Chainer*

Chainer* is a Python*-based deep learning framework aiming at flexibility and intuition. 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 supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug. Intel® optimization for Chainer, is currently integrated with the latest release of Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) 2017 optimized for Intel® Advanced Vector Extensions 2 (Intel® AVX) and Intel® Advanced Vector Extensions 512 (Intel®AVX-512) instructions which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors.

Recommended Environments

We recommend these Linux distributions.

Ubuntu 16.04 LTS 64bit
CentOS 7 64bit

The following versions of Python can be used:

2.7.10+, 3.5.2+, and 3.6.0+

Above recommended environments are tested. We cannot guarantee that Intel® optimization for Chainer works on other environments including Windows* and macOS*, even if Intel optimization for Chainer looks to be running correctly.

Install Chainer from source

You can use setup.py to install Chainer from the tarball:

$ python setup.py install

ideep4py has been split from Chainer, so you also need to install ideep4py:

$ pip install ideep4y

Use pip to uninstall chainer and ideep4py:

$ pip uninstall chainer ideep4py

Training Examples

Training test with mnist dataset:

$ cd examples/mnist
$ python train_mnist.py -g -1

Training test with cifar datasets:

run the CIFAR-100 dataset:

$ cd examples/cifar
$ python train_cifar.py –g -1 --dataset='cifar100'

run the CIFAR-10 dataset:

$ cd examples/cifar
$ python train_cifar.py –g -1 --dataset='cifar10'

Single Node Performance Test Configurations

For Single Node Performance Test Configurations, please refer to following wiki:

https://github.com/intel/chainer/wiki/Intel-Chainer-Single-Node-Performance-Test-Configurations

License

MIT License (see LICENSE file).

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

intel / chainer