Manage Deep Learning Networks with IntelChainer for Intel® Architecture

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. IntelChainer, which is optimized for Intel® architecture 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.

We recommend these Linux* distributions.

• Ubuntu* 14.04/16.04 LTS 64bit
• CentOS* 7 64bit

The following versions of Python can be used:

• 2.7.5+, 3.5.2+, and 3.6.0+

Above recommended environments are tested. We cannot guarantee that IntelChainer works on other environments including Windows* and macOS*, even if IntelChainer looks running correctly.

Before installing IntelChainer, we recommend to upgrade setuptools if you are using an old one:

$ pip install -U setuptools

The following packages are required to install Chainer.

• NumPy 1.9, 1.10, 1.11, 1.12, 1.13
• Six 1.9+
• Swig 3.0.9+

Image dataset support

• pillow 2.3+

HDF5 serialization support

• h5py 2.5+

Testing utilities

• pytest 3.2.5+

Intel® MKL-DNN

• You don’t need to manually install Intel MKL-DNN, when build IntelChainer, Intel MKL-DNN will be downloaded and built automatically, thus, boost, glog and gflags are also required.

Currently, master_v3 is the most stable version. You can use setup.py to install IntelChainer from the tarball:

$ git clone -b master_v3 https://github.com/intel/chainer
$ cd chainer
$ python setup.py install

Use pip to uninstall Chainer:

$ pip uninstall chainer

We provide the Dockerfile for Ubuntu and Centos in chainer/docker directory based on python2 and python3, respectively. Links see:

https://github.com/intel/chainer/blob/master_v3/docker/python2/Dockerfile_ubuntu

https://github.com/intel/chainer/blob/master_v3/docker/python3/Dockerfile_ubuntu

https://github.com/intel/chainer/blob/master_v3/docker/python2/Dockerfile_centos

https://github.com/intel/chainer/blob/master_v3/docker/python3/Dockerfile_centos

You can refer to following wiki to check how to build/run with docker. Even if you don’t want to play with docker, you can also refer to those docker files to learn how to build IntelChainer environment for Ubuntu and Centos based on python2 and python3.

https://github.com/intel/chainer/wiki/How-to-build-and-run-Intel-Chainer-Docker-image

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'

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

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

MIT License (see LICENSE file).

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

• Official site: http://chainer.org/
• Official document: http://docs.chainer.org/
• Pfn chainer github: https://github.com/pfnet/chainer
• Intel chainer github: https://github.com/intel/chainer
• Forum: https://groups.google.com/forum/#!forum/chainer
• Forum (Japanese): https://groups.google.com/forum/#!forum/chainer-jp
• Twitter: https://twitter.com/ChainerOfficial
• Twitter (Japanese): https://twitter.com/chainerjp
• External examples: https://github.com/pfnet/chainer/wiki/External-examples
• Research projects using Chainer: https://github.com/pfnet/chainer/wiki/Research-projects-using-Chainer