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.
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 Intel® optimization for Chainer works on other environments including Windows* and macOS*, even if Intel optimization for Chainer looks to be running correctly.
Before installing Intel Optimization for Chainer, we recommend to upgrade setuptools if you are using an old one:
$ pip install -U setuptools
The following packages are required to install IntelChainer.
- 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+
- pytest 3.2.5+
- You don’t need to manually install Intel MKL-DNN, when build Intel optimization for Chainer, Intel MKL-DNN will be downloaded and built automatically, thus, boost, glog and gflags are also required.
You can use setup.py to install Intel optimization for Chainer from the tarball:
$ python setup.py install
Use pip to uninstall Chainer:
$ pip uninstall chainer
Run with Docker*
We are providing the Docker image and Dockerfile for Ubuntu and Centos based on python2 and python3, respectively. For details see: How to build and run Intel optimization for 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'
Single Node Performance Test Configurations
For Single Node Performance Test Configurations, please refer to following wiki:
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
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.