Get Started With Intel® Distribution for Python*

Use these resources to get up to speed fast.

You can have several versions of Python on your system. Conda* also lets you manage multiple Python environments. By installing Intel Distribution for Python in a conda environment, you ensure that your system installation of Python will not be affected.

Virtualenv does not copy a required library, libpython, into the virtual directory. For details and workarounds, see this discussion on GitHub*.


We recommend managing environments with conda instead.

To set environment variables with Bash*, do one of the following:

For macOS* and Linux*:


export CC=icc


export LD_SHARED=”icc -shared”


For Windows*:


set CC=icl


set LD=xilink

To set environment variables with Bash, do one of the following:


For macOS and Linux:

export CC=icc

export LD_SHARED=”icc -shared”


For Windows:

set CC=icl

set LD=xilink

export CXX=icpc

Not directly. Many functions in NumPy and SciPy are implemented with Intel MKL functions. Since Intel Distribution for Python has the same shared libraries and functions as Intel MKL, you can build your own C extensions that link to the functions. For details, see the Intel MKL Documentation.

Yes. See INTEL_PYTHON_EULA and redist.txt in the install directory for details. This document is downloaded as part of your installation.

The –devel and –static packages can be used by developers building applications that require linking to Intel runtimes included in Intel® Distribution for Python*. For example, a developer may choose to build their own NumPy package with Intel MKL routines.

If you plan to compile against library versions in your Intel Parallel Studio XE installation, you should use the –devel packages included therein.

Starting with Intel Parallel Studio XE 2019, a local conda channel is included at the Intel Parallel Studio XE root (<psxe_installdir>/conda_channel) that contains conda packages of all Intel® Performance Libraries. Installing the libraries (with conda install mkl-devel) from that location links the libraries in Intel Parallel Studio XE into the Python environment directly from the canonical installation. This method ensures consistent library versions. We’ve added this location to the .condarc file in the intelpython env (<psxe_installdir>/intelpython), so it should work out of the box.

Intel MKL, Intel® Integrated Performance Primitives (Intel® IPP), and Intel® Data Analytics Acceleration Library (Intel® DAAL) development versions have been included in various forms for developer use. They are located in the <python_root>/pkgs directory and come as conda packages with the following naming scheme (where PROD is the product name):

PROD: shared libraries needed at runtime

PROD-include: header files

PROD-devel: shared libraries plus header files, used for building using dynamic linking

PROD-static: shared libraries plus header files, used for building using static linking

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Los compiladores Intel pueden o no optimizar al mismo nivel para los microprocesadores que no son Intel en optimizaciones que no son exclusivas de los microprocesadores Intel. Estas optimizaciones incluyen los conjuntos de instrucciones SSE2, SSE3 y SSSE3, y otras optimizaciones. Intel no garantiza la disponibilidad, funcionalidad o eficacia de ninguna optimización en microprocesadores que no sean fabricados por Intel. Las optimizaciones dependientes del microprocesador en este producto fueron diseñadas para usarse con microprocesadores Intel. Ciertas optimizaciones no específicas de la microarquitectura Intel se reservan para los microprocesadores Intel. Consulte las guías de referencia y para el usuario para obtener más información acerca de los conjuntos de instrucciones específicos cubiertos por este aviso.

Revisión del aviso n.° 20110804