Submitting GPU Jobs
Start by reading Submitting CPU Jobs page.
The GPU jobs are submitted to
To ask for a GPU card, use
-l gpu=NUMBER_OF_REQUIRED_GPUS. The submitted job has
CUDA_VISIBLE_DEVICES set appropriately, so all CUDA applications should use only the allocated GPUs.
TL;DR: You can submit a non-interactive job requiring
%M% GB RAM,
%C% CPUs (at most 2) and
%G% GPUs (at most 2, but see Quotas) by running
qsub -q gpu.q -cwd -b y -pe smp %C% -l gpu=%G%,mem_free=%M%G,act_mem_free=%M%G,h_data=%M%G path_to_binary arguments
To submit an interactive terminal, use
qrsh -q gpu.q -cwd -b y -pe smp %C% -l gpu=%G%,mem_free=%M%G,act_mem_free=%M%G,h_data=%M%G -pty yes bash -l
- Always use GPUs via qsub (or qrsh), 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. -l mem_free=8G,act_mem_free=8G,h_data=12G.
- Note that you need to use h_data instead of h_vmem for GPU jobs. CUDA driver allocates a lot of "unused" virtual memory (tens of GB per card), which is counted in h_vmem, but not in h_data. All usual allocations (malloc, new, Python allocations) seem to be included in h_data.
- Always specify the number of GPU cards (e.g. gpu=1). Thus e.g.
qsub -q gpu.q -l gpu=1
- For interactive jobs, you can use qrsh, but make sure to end your job as soon as you don't need the GPU (so don't use qrsh for long training). Warning:
-pty yes bash -lis necessary, otherwise the variable $CUDA_VISIBLE_DEVICES will not be set correctly. E.g.
qrsh -q gpu.q -l gpu=1 -pty yes bash -l
- In general: don't reserve a GPU (as described above) without actually using it for longer time, e.g., try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.
- If you know an approximate runtime of your job, please specify it with -l s_rt=hh:mm:ss - this is a soft constraint so your job won't be killed if it runs longer than specified.
CUDA and CUDNN
Default CUDA (currently 10.1 as of Nov 2019) is available in
Specific version can be found in
Depending on what version you need, you should add
LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda-X.Y/lib64:$LD_LIBRARY_PATH" to your configuration.
CUDNN is available directly in lib64 directory of the respective CUDA, so no need to configure it specifically.
Available GPU Cards
The GPU part of the cluster consists of the following nodes:
|machine||GPU type||GPU driver version||CC||GPU count||GPU RAM (GB)||CPU cores||machine RAM (GB)||remarks|
|gpu-node1||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0|
|gpu-node2||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0|
|gpu-node3||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0|
|gpu-node4||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0|
|gpu-node5||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0|
|gpu-node6||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0|
|gpu-node7||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0||only for group research|
|gpu-node8||GeForce GTX 1080||418.39||6.1||2||8.0||4||64.0||only for group research|