Hi, I'm new to slurm, so I apologize in advance if my question seems basic.
I just purchased a single node 'cluster' consisting of one 64-core cpu and an nvidia rtx5k gpu (Turing architecture, I think). The vendor supplied it with ubuntu 20.04 and slurm-wlm 19.05.5. Now I'm trying to adjust the config to suit the needs of my department. I'm trying to bone up on GRES scheduling by reading this manual page <https://slurm.schedmd.com/gres.html>, but am confused about some things. My slurm.conf file has the following lines put in it by the vendor: ################### # COMPUTE NODES GresTypes=gpu NodeName=shavak-DIT400TR-55L CPUs=64 SocketsPerBoard=2 CoresPerSocket=32 ThreadsPerCore=1 RealMemory=95311 Gres=gpu:1 #PartitionName=debug Nodes=ALL Default=YES MaxTime=INFINITE State=UP PartitionName=CPU Nodes=ALL Default=Yes MaxTime=INFINITE State=UP PartitionName=GPU Nodes=ALL Default=NO MaxTime=INFINITE State=UP ##################### So they created two partitions that are essentially identical. Secondly, they put just the following line in gres.conf: ################### NodeName=shavak-DIT400TR-55L Name=gpu File=/dev/nvidia0 ################### That's all. However, this configuration does not appear to constrain anyone in any manner. As a regular user, I can still use srun or sbatch to start GPU jobs from the "CPU partition," and nvidia-smi says that a simple cupy <https://cupy.dev/> script that multiplies matrices and starts as an sbatch job in the CPU partition can access the gpu just fine. Note that the environment variable "CUDA_VISIBLE_DEVICES" does not appear to be set in any job step. I tested this by starting an interactive srun shell in both CPU and GPU partition and running ''echo $CUDA_VISIBLE_DEVICES" and got bupkis for both. What I need to do is constrain jobs to using chunks of GPU Cores/RAM so that multiple jobs can share the GPU. As I understand from the gres manpage, simply adding "AutoDetect=nvml" (NVML should be installed with the NVIDIA HPC SDK, right? I installed it with apt-get...) in gres.conf should allow Slurm to detect the GPU's internal specifications automatically. Is that all, or do I need to config an mps GRES as well? Will that succeed in jailing out the GPU from jobs that don't mention any gres parameters (perhaps by setting CUDA_VISIBLE_DEVICES), or is there any additional config for that? Do I really need that extra "GPU" partition that the vendor put in for any of this, or is there a way to bind GRES resources to a particular partition in such a way that simply launching jobs in that partition will be enough? Thanks for your attention. Regards AR -- Analabha Roy Assistant Professor Department of Physics <http://www.buruniv.ac.in/academics/department/physics> The University of Burdwan <http://www.buruniv.ac.in/> Golapbag Campus, Barddhaman 713104 West Bengal, India Emails: dan...@utexas.edu, a...@phys.buruniv.ac.in, hariseldo...@gmail.com Webpage: http://www.ph.utexas.edu/~daneel/