Install Scipy with Mkl Through Pip

Can't install Scipy through pip

After opening up an issue with the SciPy team, we found that you need to upgrade pip with:

pip install --upgrade pip

And in Python 3 this works:

python3 -m pip install --upgrade pip

for SciPy to install properly. Why? Because:

Older versions of pip have to be told to use wheels, IIRC with --use-wheel. Or you can upgrade pip itself, then it should pick up the wheels.

Upgrading pip solves the issue, but you might be able to just use the --use-wheel flag as well.

How to enable and disable Intel MKL in numpy Python?

You can use different environments for the comparison of Numpy with and without MKL. In each environment you can install the needed packages(numpy with MKL or without) using package installer. Then on that environments you can run your program to compare the performance of Numpy with and without MKL.

NumPy doesn’t depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically Intel MKL or OpenBLAS.

• The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS.

• In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy.

• When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library.But it can also be MKL (from the defaults channel), or even BLIS or reference BLAS.

Please refer this link to know about installing Numpy in detail.

You can create two different environments to compare the NumPy performance with MKL and without it. In the first environment install the stand-alone NumPy (that is, the NumPy without MKL) and in the second environment install the one with MKL.

To create environment using NumPy without MKL.

conda create -n <env_name_1> python=<version>
conda activate <env_name_1>
pip install numpy

But depending on your OS, it might be possible that there is no distribution available (Windows).

On Windows, we have always been linking against MKL. However, with the Anaconda 2.5 release we separated the MKL runtime into its own conda package, in order to do things uniformly on all platforms.

In general you can create a new env:

conda create -n wheel_based python
activate wheel
pip install numpy-1.13.3-cp36-none-win_amd64.whl  # or whatever the file is named

In the other environment, install NumPy with MKL using below command

conda create -n <env_name_2> python=<version>
conda activate <env_name_2>
pip install intel-numpy

In these environments <env_name_1> and <env_name_2> you can run your program seperately, so that you can compare the performance of Numpy without MKL and With MKL respectively.

How to install numpy+mkl for python 2.7 on windows 64 bit?

If you do not have an entire Python distribution or you do not want to install one, you can download and install a compiled whl package from Christoph Gohlke's webpage. This whl contains numpy and is linked against mkl. When installing this package, you install both: numpy with the mkl dependencies.

All you have to do is:

• download the correct whl file (Choose the right Python version and 32/64 file)
• open a Windows cli with Windows+R and by running inside cmd
• go to the directory where you have downloaded the whl file, with cd instructions
• run pip install numpy‑1.XX.Y+mkl‑cp3X‑cp3Xm‑win_amd64.whl

For example, the command can be

pip install numpy‑1.11.3+mkl‑cp35‑cp35m‑win_amd64.whl

You can do it for any package with some code that has to be compiled

Can't install Scipy with Intel MKL

Installing redhat-rpm-config, 'Development Tools' via groupinstall solved the problem.

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