Student Cluster

This topic gives you a quick overview of the steps involved to run a job on the D-INFK student cluster.

Login in

If your course uses Jupyter then go to and log in there.

For running jobs directly and compiling software you can log in to


via secure shell (ssh).

When you log in you will be informed about the remaining GPU time per course and how much free space you have in your home directory. Keep an eye on these numbers that you do not run out of time or space before a deadline.

Running Jobs

Please read on here.


Currently there are only NVidia GTX 1080 Ti cards in the cluster:

  • 3584 CUDA cores
  • 11 GB RAM
  • Compute Capability: 6.1


You have the following general limitations on resources that you can use:

  • 20 GB of space in your home directory
  • 1 running job with
    • 1 GPU (for teams this is one GPU per team)
    • 2 CPU cores
    • 20 GB of RAM
    • 20 GB of temporary space in $TMPDIR
  • 2 jobs in the queue

The amount of hours that you have depends on the courses that you have. Each course comes with its own allowance which is displayed when you log in to a login node.

The maximum runtime per job is also specific to each course.

On the login nodes you also have the following restrictions:

  • 2 CPU cores
  • 16 GB of RAM

Expiration of Access

Access to the cluster will be disabled on the last Monday of the semester holidays. All data in your home directory will also be deleted. Please copy all data that you still need away before it will be deleted.

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