Hardware and Software Requirements for PICSciE Workshops.Requirements for PICSciE Virtual Workshops.Wa_cq_url: "/content/www/us/en/developer/articles/technical/using-intel-distribution-for-python-with-anaconda. Wa_audience: "emtaudience:business/btssbusinesstechnologysolutionspecialist/developer/softwaredeveloper", Wa_english_title: "Installing Intel® Distribution for Python* and Intel® Performance Libraries with Anaconda*", Wa_rsoftware: "rsoftware:componentsproducts/inteldistributionforpython,rsoftware:componentsproducts/intelmathkernellibrary,rsoftware:developmenttools", Wa_emtcontenttype: "emtcontenttype:designanddevelopmentreference/technicalarticle,emtcontenttype:designanddevelopmentreference/developerguide/installationguides",
Wa_emtprogramminglanguage: "emtprogramminglanguage:python", Automatically installed along with development packages OneMKL static libraries and headers for building software OneMKL dynamic runtimes and headers for building software Intel® oneAPI Math Kernel Library (oneMKL) dynamic runtimes The following table lists the available packages with a brief description for their contents: Package Name Then install any of our available performance libraries using "conda install" as normal, such as: conda install mkl-devel Make sure the Intel channel is added to your conda configuration (see above). We have published them as conda packages for your convenience. 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. 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 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. For example, to install affine do: conda install affineĪvailable Intel packages can be viewed here: Using Intel Conda* Packages with Continuum's Python* Non-intel packages are installed as usual. For example, to install intel sympy do: conda install sympy You can use the usual conda install commands for additional packages. You now have the core environment, including python, numpy, scipy.
Linux/macOS users do: source activate idpĪnd Microsoft Windows users do: activate idp Then follow the usual directions for activating the environment.
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.xįor example, for Python* version 3.7: conda create -n idp intelpython3_full python=3.7 If you want python 2 version do: conda create -n idp intelpython2_core python=2
Please note that " x " in " python=3.x " should signify which version of Python* you would like to install.įor example, for Python* version 3.7: conda create -n idp intelpython3_core python=3.7 To install the core python3 environment, do: conda create -n idp intelpython3_core python=3.x
We recommend that you create a new environment when installing. conda config -add channels intel Installing the Intel® Distribution for Python* Tell conda to choose Intel packages over default packages, when available.
You need at least conda 4.1.11, so first update your conda. 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*.