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We do provide Python installations and various packages (i.e. scipy, numpy, pandas, matplotlib etc.) based on the intel or gcc compiler, which are optimized to perform very well on the cluster. If you need to install Python packages yourself, we will explain how to do so in this article. If you run into problems you can always contact support at There are some occasions, where anything other than conda fails but we would like to try every other option before going this route.

Thank you.

Installing additional Python packages

You may want to install Python libraries that are not provided by the default  installation or any other module. These can be installed directly to your home directory without any admin privileges using the following procedure.  Note that some packages might need external libraries. Requirements can usually be found in the package documentation. These libraries have to be loaded before by using the module load command.



When switching to different Python versions in the future, you might have to re-install your personal packages!

Installing additional Perl modules

1. Load the base Perl module from the intel or foss toolchain

Code Block
module load foss/2019a
module load Perl

2. Install the module using the cpanm command:

Code Block
cpanm --local-lib=~/perl5 local::lib && eval $(perl -I ~/perl5/lib/perl5/ -Mlocal::lib)
cpanm _module_name_

To use this module, you have to call

Code Block
eval $(perl -I ~/perl5/lib/perl5/ -Mlocal::lib)

to set the @INC-path to make it findable.