Hi,
if by "share the GPU" you mean exclusive allocation to a single job then, I believe, you are missing cgroup configuration for isolating access to the GPU. Below the relevant parts (I believe) of our configuration. There also is a way of time- and space-slice GPUs but I guess you should get things setup without slicing. I hope this helps. Manuel ==> /etc/slurm/cgroup.conf <== # https://bugs.schedmd.com/show_bug.cgi?id=3701 CgroupMountpoint="/sys/fs/cgroup" CgroupAutomount=yes AllowedDevicesFile="/etc/slurm/cgroup_allowed_devices_file.conf" ==> /etc/slurm/cgroup_allowed_devices_file.conf <== /dev/null /dev/urandom /dev/zero /dev/sda* /dev/cpu/*/* /dev/pts/* /dev/nvidia* ==> /etc/slurm/slurm.conf <== ProctrackType=proctrack/cgroup # Memory is enforced via cgroups, so we should not do this here by [*] # # /etc/slurm/cgroup.conf: ConstrainRAMSpace=yes # # [*] https://bugs.schedmd.com/show_bug.cgi?id=5262 JobAcctGatherParams=NoOverMemoryKill TaskPlugin=task/cgroup JobAcctGatherType=jobacct_gather/cgroup -- Dr. Manuel Holtgrewe, Dipl.-Inform. Bioinformatician Core Unit Bioinformatics – CUBI Berlin Institute of Health / Max Delbrück Center for Molecular Medicine in the Helmholtz Association / Charité – Universitätsmedizin Berlin Visiting Address: Invalidenstr. 80, 3rd Floor, Room 03 028, 10117 Berlin Postal Address: Chariteplatz 1, 10117 Berlin E-Mail: manuel.holtgr...@bihealth.de Phone: +49 30 450 543 607 Fax: +49 30 450 7 543 901 Web: cubi.bihealth.org www.bihealth.org www.mdc-berlin.de www.charite.de ________________________________ From: slurm-users <slurm-users-boun...@lists.schedmd.com> on behalf of Analabha Roy <hariseldo...@gmail.com> Sent: Wednesday, February 1, 2023 6:12:40 PM To: slurm-users@lists.schedmd.com Subject: [ext] [slurm-users] Enforce gpu usage limits (with GRES?) 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<mailto:dan...@utexas.edu>, a...@phys.buruniv.ac.in<mailto:a...@phys.buruniv.ac.in>, hariseldo...@gmail.com<mailto:hariseldo...@gmail.com> Webpage: http://www.ph.utexas.edu/~daneel/
