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I am using PIP to install Scipy with MKL to accelerate the performance. My OS is Ubuntu 64 bit. Using the solution from this question, I create a file .numpy-site.cfg
[mkl] library_dirs=/opt/intel/composer_xe_2013_sp1/mkl/lib/intel64/ include_dirs=/opt/intel/mkl/include/ mkl_libs=mkl_intel_lp64,mkl_intel_thread,mkl_core,mkl_rt lapack_libs=
This file helps me to install Numpy with MKL successfully. However, using the same above file, installing Scipy prompts the error
ImportError: libmkl_rt.so: cannot open shared object file: No such file or directory
I also use
but the problem is still the same.
Anyone know how to fix this problem? I don’t want to install Scipy manually so anyone give me some hints to fix it.
Intel has been publishing wheels of packages like Numpy, Scipy and Scikit-learn to PyPI. These wheels have been built while linking against Intel MKL, and include various optimizations.
If you want Scipy built with Intel MKL:
#Remove existing Numpy and/or Scipy: pip uninstall numpy scipy -y #Install scipy built with Intel MKL: pip install intel-scipy
2 years have passed since this question was asked.
There are now numpy/scipy wheels for linux that use a openblas compiled for avx2, so you can get much better performance without building packages. You may need to upgrade pip to get it to install the wheel:
pip install --upgrade pip pip install numpy scipy
If you want MKL, then you can install Anaconda or Intel Distribution for Python. They use conda instead of pip to manage packages, but they are free and distribute packages that contain all the dependences, including MKL.
I have Win10 64Bit with Python 3.6.2 i have installed scipy through http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
I followed following steps :
- Step 1: Uninstall if you have any previous version of numpy,
pip uninstall numpy
- Step 2 : Download numpy?1.13.1+mkl?cp36?cp36m?win_amd64.whl with
MKL(Math Kernel Library) from below link,
- Step 3: copy downloaded file into another location and launch command
prompt from that location.
- Step 4: run this command,
pip install -U numpy-1.13.1+mkl-cp36-cp36m-win_amd64.whl
- Step 5: Now Download scipy library from,
- Step 6: Copy downloaded file into same location in which numpy is
- Step 7: In CMD prompt run this cmd,
pip install scipy-0.19.1-cp36-cp36m-win_amd64.whl
If you are having trouble installing or running with specific version then first uninstall and then install
pip uninstall -v numpy
Step 2: download the wheel file and install
pip install -U numpy-1.13.0+mkl-cp36-cp36m-win_amd64.whl
In this example wheel file name is “numpy-1.13.0+mkl-cp36-cp36m-win_amd64.whl
Since the actual question itself was not answered, let me give it a shot…
I think the problem here basically is that the BLAS/LAPACK libraries being used are spread out across multiple location, and numpy doesn’t handle this well.
We have fixed this in EasyBuild, where we have been building numpy/scipy on top of Intel MKL for a while now, with this patch: https://github.com/hpcugent/easybuild-easyconfigs/blob/master/easybuild/easyconfigs/n/numpy/numpy-1.8.1-mkl.patch
For me, MKL came with Scipy library by
conda install scipy
@rscohn2 solution gave me a hint for using packages.
For the reference, my environment is included Ubuntu, Anaconda, Python 3.6, Scipy 1.1 and MKL 2018.0.