For Head TAs

This topic contains information for head TAs that intend to use the cluster for a course that they are responsible for.

Please note that the cluster is only for GPU jobs. For plain CPU jobs we recommend going to Euler and use the free tier.


Before we can even set up the environment we need the following information.

Course Tag

We need a tag for your course, either an abbreviation or something short. aml or mixed_reality would be fine.

The tag will be used for course folder and SLURM account names as well as for the access groups we will create.

Number of Students

We only have 256 GPUs. For large classes we may need to build teams. In this case we need to know which students form a team. If you give us a ratio we can also automatically assign students to teams.

Maximum Job Runtime

Currently we have four different priorities for job run time and you will need to chose one that fits best for your course:

Has priority over everything else, maximum 60 min runtime, fills cluster completely if necessary. This is intended for running Jupyter notebooks or interactive sessions and nothing else.
10 min runtime
60 min runtime
Negotiable limit, but jobs are preempted by Interactive/Short/Medium priority jobs and automatically restarted. Checkpointing is heavily recommended!

Please consider what the longest runtime of a job of your students is going to be.

Total number of Job Hours for a Student

How much time will a student (or a team) need to comfortably complete all exercises of a semester? Each student (or team) will not be able to run new jobs if the amount of time is used up.

We do not want this number to be too high to encourage using the resources efficiently.


Each course gets a folder /cluster/courses/{course tag} where you can put data sets or whatever the students need. This folder will be writable by the TAs and readable by the students.

If you have large data (>500GB) then please contact us. We may need to offload this data to a different, slower location.


We only install software that comes with Ubuntu. If you miss something then please let us know. Login nodes and compute nodes have the same software installed.

You can compile your own software to /cluster/courses/{course tag} and have your students include a location there in their $PATH. Same goes for singularity images or Jupyter environments.

Python and Anaconda

We recommend that you set up a python virtual environment or anaconda installation under /cluster/courses/{course tag} with all the packages installed for your particular course. This is especially preferred if you need something like pytorch that requires multiple gigabytes. Then tell your students how to activate the environment.

Students can of course set up their own python environment or install anaconda in their home directory, but that will use up a lot of disk space.


To use Jupyter with requires you to setup of a Jupyter environment under /cluster/courses/{course tag} that we will enable in the chooser for environment of the hub.

Please contact us in advance as this requires some additional manual steps.

Additional Information

To complete the setup for production we also need the following but it's OK to supply it at a later point of time:

  • The list of student logins or mail addresses, for instance from eDoz.
  • The list of additional TA logins

At the moment you need to report us users that have to be added.

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