Intel® Optimization for Chainer*

Chainer Backend for Intel Architecture, a Chainer module providing numpy like API and DNN acceleration using MKL-DNN.

Requirements

This preview version is tested on Ubuntu 16.04, Centos 7.4 and OS X.

Minimum requirements:

cmake 3.0.0+
C++ compiler with C++11 standard support (GCC 5.3+ if you want to build tests)
Python 2.7.6+, 3.5.2+, 3.6.0+
Numpy 1.13
Swig 3.0.12
Doxygen 1.8.5
(optional) MPICH devel 3.2

Other requirements:

Testing utilities
- Gtest
- pytest

Installation

Install setuptools:

If you use old setuptools, upgrade it:

pip install -U setuptools

Install python package from the source code:

CentOS:

git submodule update --init && mkdir build && cd build && cmake3 ..
cd ../python
python setup.py install

Other:

git submodule update --init && mkdir build && cd build && cmake ..
cd ../python
python setup.py install

Install python package via PYPI:

pip install ideep4py

Since Python3.7 doesn't work with numpy==1.13, we built iDeep4py Python3.7 wheel based on numpy==1.16.0, remember to upgrade numpy version to 1.16.0 before install iDeep4py Python3.7 wheel. Suggest installing Python package using virtualenv to avoid installing Python packages globally which could break system tools or other projects.

Install python package via Conda:

conda install -c intel ideep4py

Install python package via Docker:

We are providing the official Docker images based on different platforms on Docker Hub.

docker pull chainer/chainer:latest-intel-python2
docker run -it chainer/chainer:latest-intel-python2 /bin/bash

Multinode support:

Non-blocking multinode data parallelism is supported. The system is requried to meet MPICH dependency and user needs to replace the cmake command in build process:

Make sure your MPI executable is in PATH:

PATH=$PATH:<path-to-mpiexec>
# use the following line when you execute cmake or cmake3
# CentOS:
cmake3 -Dmultinode=ON ..
# Other:
cmake -Dmultinode=ON ..

Execute the test:

cd total_reduce/test
mpirun -N 4 python3 test_1payload_inplace.py

The commands above will start 4 MPI processes on your machine and conduct a blocking allreduce operation among all 4 processes. To test it in a real multinode environment, compile your file and use the following commands:

cd total_reduce/test
mpirun -f <hostlist> -N 4 python3 test_1payload_inplace.py

More information

MKL-DNN github: https://github.com/01org/mkl-dnn
Chainer github: https://github.com/chainer/chainer

License

MIT License (see LICENSE file).

jiayisunx/ideep