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Currently, we have a variety of Python-Versions available as module files. To list them all run

$ module avail|& grep 'lang/Python'

The Python-Versions available as module files, do provide numpy, scipy, pandas, cython and more. However, especially a matplotlib module is most likely missing. This is because our installation framework installs it separately. Hence, the matplotlib functionality has to be loaded as an additional functionality as a module file.

The intel versions are link against Intel's MKL. Exporting OMP_NUM_THREADS enables multithreaded matrix handling with numpy.

If you intend to use Python in combination with another module, ensure that the toolchain and the toolchain version of the additional module fit with your selected Python module. With regard to the Python version, try to stay as current as possible.

If you need additional Python packages, you can easily install them yourself either "globally" in your home directory or inside of a virtual environment.

Your Personal Environment (Additional Packages)

In general, having a personal Python environment where you can install third-party packages (without needing root priviliges) yourself is very easy. The preparation steps needed on Mogon are described below. While the first variant is already sufficient, we recommend using virtualenvs since they are a lot easier to work with. Virtualenvs can also be shared between users if created in your groups project directory, but most importantly virtual environments bear the potential to avoid the setup hell you might otherwise experience.

First, create some directories in which installed packages will be placed:

$ mkdir -p ~/.local/bin
$ mkdir -p ~/.local/lib/python<VERSION>/site-packages

Then add the created bin directory to your PATH in your .bashrc file and source it:

$ echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
$ source ~/.bashrc

Now create a configuration file for easy_install and pip, the Python package management tools:

$ echo -e '[easy_install]\nprefix = ~/.local' > ~/.pydistutils.cfg
$ mkdir -p ~/.pip
$ echo -e '[install]\nuser = true' > ~/.pip/pip.conf

If you now use easy_install or pip, it will automatically install packages to the correct paths in your home directory.

A so called virtualenv can be seen as an isolated, self-contained Python environment of third-party packages.
Different virtualenvs do not interfere with each other nor with the system-wide installed packages.

It is advised to make use of virtualenv in Python, especially if you intend to install different combinations or versions of various Python packages. Virtualenvs can also be shared between users if created in your groups project directory.

If you are using Python 2.6.6, you need to install virtualenv:

$ easy_install virtualenv
Searching for virtualenv
Best match: virtualenv 1.10.1
Processing dependencies for virtualenv
Finished processing dependencies for virtualenv

We need to remove the easy_install configuration file created above, since the path set there would interfere with virtualenv:

$ rm ~/.pydistutils.cfg
$ rm ~/.pip/pip.conf

Now you can simply create, activate, use, deactivate and destroy as many virtualenvs as you want:


Creating a virtualenv will simply set up a directory structure and install some baseline packages:

$ virtualenv ENV
New python executable in ENV/bin/python
Installing Setuptools...done.
Installing Pip...done.

With virtualenvs, you can even make each virtualenv use its own version of the Python interpreter:

$ virtualenv --python=/usr/bin/python2.6 --system-site-packages ENV2.6
$ virtualenv --python=/cluster/Apps/Python/<VERSION>/bin/python --system-site-packages ENV<VERSION>

If you want to install the pre-installed third-party packages (numpy, scipy, matplotlib, etc.) yourself, just omit the –system-site-packages parameter when calling virtualenv.


To work in a virtualenv, you first have to activate it, which sets some environment variables for you:

$ source ENV/bin/activate
(ENV)$ # Note the name of the virtualenv in front of your prompt - nice, heh?


Now you can use your virtualenv - newly installed packages will just be installed inside the virtualenv and just be visible to the python interpreter you start from within the virtualenv:

(ENV)$ easy_install requests
Searching for requests
Best match: requests 1.2.3
Processing dependencies for requests
Finished processing dependencies for requests


(ENV)$ pip install requests
Downloading/unpacking requests
  Downloading requests-1.2.3.tar.gz (348kB): 348kB downloaded
  Running egg_info for package requests
Installing collected packages: requests
  Running install for requests
Successfully installed requests
Cleaning up...

And now compare what happens with the python interpreter from inside the virtualenv and with the system python interpreter:

(ENV)$ python -c 'import requests'
(ENV)$ /usr/bin/python -c 'import requests'
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ImportError: No module named requests


Deactivating a virtualenv reverts the activation step and all its changes to your environment:

(ENV)$ deactivate


To destroy a virtualenv, simply delete its directory:

$ rm ENV

Using multiple virtualenvs can be made much more user friendly using virtualenvwrapper.

If you are using Python 2.6.5, you can install and configure it using

$ easy_install --prefix=$HOME/.local virtualenvwrapper
$ echo 'source $HOME/.local/bin/' >> ~/.bashrc

If you are using any other version of Python, virtualenvwrapper is already installed and you just need to

$ echo 'source /cluster/Apps/Python/<VERSION>/bin/' >> ~/.bashrc

Re-login to apply the changes.

Load Environment Modules (module load [mod])

To load environment modules in python:


Job submission

Like with other interpreted languages, you can indicate to the desired language for interpreting the script using a shebang. Here is an example script. Obviously, you can adapt the submit()-function for your needs (e.g. add logging functionality, account better / differently for multithreading, etc.):

#!/bin/env python
#SBATCH -p nodeshort
#SBATCH -A <your account>
#SBATCH -n 32 # assuming 2-threaded daughter processes
              # otherwise specify do not '-c'
              # (will be set to 1, implicitely)
#SBATCH -c 2  # number of cores per task, e.g. 2 threads
#SBATCH -t 10
#SBATCH -J python-demo
#SBATCH -o python-demo.%j.log
import subprocess
import shlex
import locale
import os
import glob
def submit(call, ignore_errors = False):
    n_threads = os.environ['SLURM_CPUS_PER_TASK']
    os.environ['OMP_NUM_THREADS'] = n_threads
    if int(n_threads) > 1: 
        call = 'srun -n 1 -c %s --hint=multithread --cpu_bind=q %s' % (n_threads, call)
        call = 'srun -n 1 %s' % call
    call = shlex.split(call)
    process = subprocess.Popen(call, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    out, err = process.communicate()
    out = out.decode(locale.getdefaultlocale()[1])
    err = err.decode(locale.getdefaultlocale()[1])
    if (not ignore_errors) and (process.returncode):
        print("call failed, call was: %s" % ' '.join(call))
        print("Message was: %s" % str(out))
        print("Error code was %s, stderr: %s" % (process.returncode, err))
    return process.returncode, out, err
if __name__== '__main__':
    for fname in glob.glob('*.input'):
        call = "your application --threads=2 --infile=%s" % fname

For multinode scripts, ensure that the environment is set remotely (for most cases srun takes care of it).

Scripts employing mpi4py should not submit themselves. Scripts employing Python's onboard multiprocessing module do not need the submit()-function, obviously.

Performance Hints

Many of the hints are inspired by O'Reilly's Python Cookbook chapter on performance. We only discuss very little here explicitly, it is worth reading this chapter. If you need help getting performance out of Python scripts contact us.

Better than guessing is to profile, how much time a certain program or task within this program takes. Guessing bottlenecks is a hard task, profiling often worth the effort. The above mentioned Cookbook covers this chapter.

Avoid them as much you can. If you have to use them, compile them, prior to any looping, e.g.:

import re
myreg = re.compile('\d')
for stringitem in list:, stringitem)
   # or

A little-known fact is that code defined in the global scope like this runs slower than code defined in a function. The speed difference has to do with the implementation of local versus global variables (operations involving locals are faster). So, if you want to make the program run faster, simply put the scripting statements in a function (also: see O'Reilly's Python Cookbook chapter on performance).

The speed difference depends heavily on the processing being performed.

Every use of the dot (.) operator to access attributes comes with a cost. Under the covers, this triggers special methods, such as getattribute() and getattr(), which often lead to dictionary lookups.

You can often avoid attribute lookups by using the from module import name form of import as well as making selected use of bound methods. See the illustration in O'Reilly's Python Cookbook chapter on performance.

To avoid constant flushing (particularly in Python 2.x) and use buffered output instead, either use Python's logging module instead as it supports buffered output. An alternative is to write to sys.stdout and only flush in the end of a logical block.

In Python 3.x the print()-function comes with a keyword argument flush, which defaults to False. However, use of the logging module is still recommended.

Any constant scalar is best not calculated in any loop - regardless of the programming language. Compilers might(!) optimize this away, but are not always capable of doing so.

One example (timings for the module tools/IPython/6.2.1-foss-2017a-Python-3.6.4 on Mogon I, results on Mogon II may differ, the message will hold):

Every trivial constant is re-computed, if the interpreter is asked for this:

In [1]: from math import pi
In [2]: %timeit [1*pi for _ in range(1000)]
149 µs ± 6.5 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [3]: %timeit [pi for _ in range(1000)]
87.1 µs ± 2 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

The effect is more pronounced, if division is involved:

In [4]: some_scalar = 300
In [5]: pi_2 = pi / 2
In [6]: %timeit [some_scalar / (pi / 2) for _ in range(1000)]
249 µs ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [7]: %timeit [some_scalar / pi_2 for _ in range(1000)]
224 µs ± 5.62 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Solution: Some evaluations are best placed outside of loops and bound to a variable.

Remember that every Python Module on Mogon comes with Cython. Cython is an optimising static compiler for both the Python programming language and the extended Cython programming language.

While we cannot give a comprehensive intro in this wiki document, we recommend using Cython whenever possible and give this little example:

Imaging you have a (tested) script, you need to call frequently. Then create modules your main script can import and write a setup script like this:

# script: 
#!/usr/bin/env python
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
named_extension = Extension(
    "name of your extension",
    include_path = ['/cluster/Apps/Python3.4/include/python3.4m']
    name = "some_name",
    cmdclass = {'build_ext': build_ext},
    ext_modules = [named_extension] 

Replace named_extension with a name of your liking, and fill-in all place holders. You can now call the setup-skript like this:

$ python ./ build_ext --inplace

This will create a file directory_of_your_module/<module_name1>.c and a file directory_of_your_module/<module_name1>.so will be the result of a subsequent compilation step.

In Cython you can release the global interpreter lock (GIL), see this document (scroll down a bit), when not dealing with pure python objects.

In particular Cython works with ''numpy''.

Profiling memory is a special topic on itself. There is, however, the Python module "memory profiler", which is really helpful if you have an idea where to look. There is also Pympler, yet another such module.

Things to consider

Python is an interpreted language. As such it should not be used for lengthy runs in an HPC environment. Please use the availability to compile your own modules with Cython; consult the relevant Cython documentation. If you do not know how to start, attend a local Python course or schedule a meeting at our local HPC workshop.

Special packages

Please note that we have already installed numpy, scipy and matplotlib in the versions of Python that we provide additionally.

When installing NumPY, the first installation attempt fails at exit. Don't worry, the installation is already finished then, but to be sure, you can simply run the command again to see it exiting cleanly.

Note that NumPY can also be linked against the Intel Math Kernel Library or the AMD Core Math Library:

  • software/python.1526304059.txt.gz
  • Last modified: 2018/05/14 15:20
  • by meesters