Installing Intel® Distribution for Python* and Intel® Performance Libraries with Anaconda*

Published: 05/28/2016  

Last Updated: 03/28/2022

By Robert S Cohn

We have worked with Continuum Analytics* to make it easy to use Intel® Distribution for Python and the Intel® Performance Libraries (such as Intel® oneAPI Math Kernel Library (oneMKL)) with the Conda* package manager and Anaconda Cloud*. You need at least conda 4.1.11, so first update your conda.

conda update conda

Tell conda to choose Intel packages over default packages, when available.

conda config --add channels intel

Installing the Intel® Distribution for Python*

We recommend that you create a new environment when installing. To install the core python3 environment, do:

conda create -n idp intelpython3_core python=3.x

Please note that "x" in "python=3.x" should signify which version of Python* you would like to install.

For example, for Python* version 3.7:

conda create -n idp intelpython3_core python=3.7


If you want python 2 version do:

conda create -n idp intelpython2_core python=2


If you want the full Intel distribution, replace the "core" package name with "full", like this for python3:

conda create -n idp intelpython3_full python=3.x

Please note that "x" in "python=3.x" should signify which version of Python* you would like to install.

For example, for Python* version 3.7:

conda create -n idp intelpython3_full python=3.7


Then follow the usual directions for activating the environment. Linux/macOS users do:

source activate idp

and Microsoft Windows users do:

activate idp

You now have the core environment, including python, numpy, scipy,... You can use the usual conda install commands for additional packages. For example, to install intel sympy do:

conda install sympy

Non-intel packages are installed as usual. For example, to install affine do:

conda install affine

Available Intel packages can be viewed here:

Using Intel Conda* Packages with Continuum's Python*

If you want to install Intel packages into an environment with Continuum's python, do not add the "intel" channel to your configuration file because that will cause all your Continuum packages to be replaced with Intel builds, if available. Rather, specify the "intel" channel on the command line with "-c intel" parameter and the "--no-update-deps" flag to avoid switching other packages, such as python itself, to Intel's builds:

conda install mkl -c intel --no-update-deps
conda install numpy -c intel --no-update-deps

Installing the Intel® Performance Libraries

If you want to build a native extension that directly uses the performance libraries, then you will need to obtain a development package that contains header files and static libraries. We have published them as conda packages for your convenience. 

Make sure the Intel channel is added to your conda configuration (see above). Then install any of our available performance libraries using "conda install" as normal, such as:

conda install mkl-devel

The following table lists the available packages with a brief description for their contents:

Package Name Lin‑64 Lin‑32 Win‑64 Win‑32 macOS‑64 Description
mkl X X X X X Intel® oneAPI Math Kernel Library (oneMKL) dynamic runtimes
mkl‑devel X X X X X oneMKL dynamic runtimes and headers for building software
mkl‑static X X X X X oneMKL static libraries and headers for building software
mkl‑include X X X X X oneMKL headers only. Automatically installed along with development packages


Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at