Benchmarked Libraries

Benchmarking is an exact science as the results may change depending on the machine used to compute the figures. There is not necessarily an exact correlation between the processing time and the algorithm cost. The results may also depend on the options used to compile a library (CPU, GPU, MKL, …). Next sections gives some details on how it was done.

scikit-learn

scikit-learn is usually the current latest stable version except if the test involves a pull request which implies scikit-learn is installed from the master branch.

onnxruntime

onnxruntime is not easy to install on Linux even on CPU. The current implementation requires that Python is built with a specific flags --enable-shared:

./configure --enable-optimizations --with-ensurepip=install --enable-shared --prefix=/opt/bin

This is due to a feature which requests to be able to interpret Python inside a package itself and more specifically: Embedding the Python interpreter. Then the environment variable LD_LIBRARY_PATH must be set to the location of the shard libraries, /opt/bin in the previous example. The following issue might appear:

UserWarning: Cannot load onnxruntime.capi.
Error: 'libnnvm_compiler.so: cannot open shared object file: No such file or directory'

To build onnxruntime:

git clone https://github.com/Microsoft/onnxruntime.git --recursive

export LD_LIBRARY_PATH=/usr/local/Python-3.7.2
export PYTHONPATH=/home/dupre/xadupre/onnxruntime/build/debian/Release
python3.7 ./onnxruntime/tools/ci_build/build.py --build_dir ./onnxruntime/build/debian --config Release --build_wheel --use_mkldnn --use_openmp --use_llvm --numpy_version= --skip-keras-test

Build mkl-dnn

onnxruntime requires MKL-DNN (or Math Kernel Library for Deep Neural Networks) if flags --use_mkldnn is used. It can be built like the following:

git clone https://github.com/intel/mkl-dnn.git
cd scripts && ./prepare_mkl.sh && cd ..
mkdir -p build && cd build && cmake $CMAKE_OPTIONS ..
make
ctest
make install