Preface

We provide several ways of using python at LRZ which have all different ways and ability for customization by the user

Python installed

OS installed python (likely wrong choice)

After login without loading modules or custom environments an OS-provided python will be available.

which python

In case the answer is /usr/bin/python , then this is the version of python that comes by the OS. It is rather old but stable.

Typically this isn't the right choice for your scientific workload: probably not to use!

Module provided python

Check with the module avail command for the actually available version. Like,

module av python

---------------------------------------------- /lrz/sys/spack/release/22.2.1/modules/x86_64/linux-sles15-x86_64 ----------------------------------------------
python/3.7.11-base  python/3.7.11-extended  python/3.8.11-base(default)  python/3.8.11-extended  

--------------------------------------------------------- /lrz/sys/share/modules/files_sles15/tools ----------------------------------------------------------
python/2.7_intel  python/3.6_intel 

Python compiled within the spack-stack (module-default)

Starting June 2021, two types of python modules are provided via the spack stack

• python/<version>-base
• python/<version>-extended 

The version that is used by applications provided by spack is set to default.

The flavors (base & extended) differ on the libraries provided with them  (check `ls $PYTHON_BASE/.spack/py-*` after loading the module):

• • <version>-base
    comes with only a few libraries (e.g. ipython, autopep8, ...)
  • <version>-extended
    additionally provides libraries like mpi4py, numpy, and scipy that have been compiled against Intel-MPI & Intel-MKL, respectively.

• these python installations are meant to be complemented with additional libraries via pip
  within your own user-space if required py your python- workflow or code
  typically this is done inside a virtual-environment (see next section)

PIP: extending python in a virtual environment using pip

In case you need additional packages for your work - you may use PIP inside a virtual environment and install python-packages in addition inside such a virtual environment

# e.g.:
module load python/3.8.11-base
python3 -m venv ~/venv/py38
. ~/venv/py38/bin/activate
pip3 install psutil

python3
# alternative python

Python 3.8.11 (default, Jan 26 2022, 17:51:41) 
[GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

then you can load the just installed psutil inside python

import psutil

You may find about which packages may be installed using pip-command in a shell or consult python-package-index on the web

you may deactivate your virtual-environment with

deactivate

Intel python modules (treated as deprecated from our side)

Two legacy modules with conda-based Intel-Python (currently versions 2.7_intel and 3.6_intel) are provided. They serve to support older python programs with a working version. Intel python includes support for Intel-MPI and Intel-MKL.

Note that python/2.7_intel was the default module in the past. From June 2021 this is no longer the case and you have to specify this version explicitly upon loading. This is particularly important if your python program is not python 3.x compatible.

The version is provided 'as is'

Conda: Installing python & python packages using conda

With spack stack 22.2.1 modules for anaconda and miniconda are provided. After loading either of these you can download the latest python & python packages via conda. For that, you have to generate your own environment by

module load anaconda3
# alternative: module load miniconda3

conda create -n py38 python=3.8

now you can activate your environment  ...

and install the package you like

source activate py38
conda install numpy

then you can load the just installed numpy inside python

python3
# alternative python

Python 3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) 
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

import numpy

Find available packages via conda-command in your shell or web-search or conda-related documentation on the web (e.g. conda-forge-repo, conda-intel-repo)

deactivate conda-environment with

conda deactivate

Python on SuperMUC-NG

SuperMUC-NG does not allow for outgoing internet connections and therefore conda does not work out of the box (same for pip or R-CRAN) and you have to use a tunnel or use conda pack.
Please consult our FAQ section: FAQ: Python and R on SuperMUC-NG by using conda