Submitting GPU Jobs


Start by reading Submitting CPU Jobs page.

The GPU jobs are submitted to gpu partition.

To ask for one GPU card, use #SBATCH --gres=gpu:1 directive or --gres=gpu:1 option on the command line. The submitted job has CUDA_VISIBLE_DEVICES set appropriately, so all CUDA applications should use only the allocated GPUs.


  • Always use GPUs via sbatch (or srun), never via ssh. You can ssh to any machine e.g. to run nvidia-smi or htop, but not to start computing on GPU.
  • Don't forget to specify you RAM requirements with e.g. --mem=10G.
  • Always specify the number of GPU cards (e.g. --gres=gpu:1). Thus e.g. srun -p gpu --mem=64G --gres=gpu:2 --pty bash
  • For interactive jobs, you can use srun, but make sure to end your job as soon as you don't need the GPU (so don't use srun for long training).
  • In general: don't reserve a GPU (as described above) without actually using it for longer time, e.g., try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.
  • If you know an approximate runtime of your job, please specify it with -t . Acceptable time formats include "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes" and "days-hours:minutes:seconds".

CUDA and cuDNN

Available CUDA versions are in


CUDA modules

You can load late versions of CUDA as modules. This will set various environment variables for you so you should be able to use CUDA easily.

  1. list available modules with: module avail
  2. load the version you need (possibly specifying the version of CuDNN): module load <modulename>
  3. you can unload the module with: module unload <modulename>