Intel Python

Intel's distribution of optimized python is available on ARGO. Intel has optimized the most commonly used python packages, i.e., Numpy, Scipy, Scikit-learn using Intel MKL (Math Kernel Library) and Intel Data Analytics Acceleration (DAAL) libraries. On Intel architecture these optimized packages are expected to provide a significant performance boost. To make use of Intel's distribution of packages users need to load the Intel module 'intel/python/2018-10-p36' for Python version 3.6 or 'intel/python/2018-10-p27' for Python version 2.7.

$ module load intel/python/2018-10-p36
$ pip freeze
daal==2019.0
icc-rt==2019.0
impi==2019.0
intel-numpy==1.15.1
intel-openmp==2019.0
intel-scikit-learn==0.19.2
intel-scipy==1.1.0
...

In order to check whether the intel optimized version of numpy and other packages has been loaded, the configuration of the packages can be checked as follows,

$ python3
Python 3.6.4 (default, Jun  7 2018, 10:05:32)
[GCC 7.3.1 20180303 (Red Hat 7.3.1-5)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.show_config()
mkl_info:
   libraries = ['mkl_rt', 'pthread']
   library_dirs = ['/opt/anaconda1anaconda2anaconda3/lib']
   define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
   include_dirs = ['/opt/anaconda1anaconda2anaconda3/include']

Optimized Tensorflow and Caffe

Intel-optimized Tensorflow and Caffe are also available on ARGO. Users are encouraged to use these optimized versions if the code is written for cpus only.