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 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 is not easy to install on Linux even on CPU.
The current implementation requires that Python is built
with a specific flags
./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
cannot import name ‘get_all_providers’
The following error usually indicates than onnxruntime was compiled on one machine and used on another one with different dynamic libraries. Missing libraries needs to be installed or onnxruntime must be compiled on the machine it needs to be used.
ImportError: cannot import name 'get_all_providers' from 'onnxruntime.capi._pybind_state'
onnxruntime requires MKL-DNN
(or Math Kernel Library for Deep Neural Networks)
--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