We have worked with Continuum Analytics* to make it easy to use Intel® Distribution for Python and the Intel® Performance Libraries (such as Intel® Math Kernel Library (Intel® MKL)) 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
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
If you want python 2 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
Then follow the usual directions for activating the environment. Linux/macOS users do:
source activate idp
and Microsoft Windows users do:
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: https://anaconda.org/intel/packages
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
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:
|mkl||X||X||X||X||X||Intel® Math Kernel Library (Intel® MKL) dynamic runtimes|
|mkl‑devel||X||X||X||X||X||Intel® MKL dynamic runtimes and headers for building software|
|mkl‑static||X||X||X||X||X||Intel® MKL static libraries and headers for building software|
|mkl‑include||X||X||X||X||X||Intel® MKL headers only. Automatically installed along with development packages|
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804