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.

Rules

• 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

Default CUDA (currently 11.2 as of Nov 2021) is available in

 /opt/cuda

Specific version can be found in

 /lnet/aic/opt/cuda/cuda-{9.0,9.2,10.0,10.1,10.2,11.2,...}

Depending on what version you need, you should add LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda-X.Y/lib64:$LD_LIBRARY_PATH" to your configuration.

Regarding cuDNN:

• for CUDA 9.0, 9.2, 10.0 and 10.1, cuDNN is available directly in lib64 directory of the respective CUDA, so no need to configure it specifically;
• for CUDA 10.1 and later, cuDNN is available in cudnn/VERSION/lib64 subdirectory of the respective CUDA, so you need to add LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda-X.Y/cudnn/VERSION/lib64:$LD_LIBRARY_PATH" to your configuration.