Chaofeng, I agree with what Chris says. You should be using cgroups. I did a lot of work with cgroups anf GPUs in PBSPro (yes I know... splitter!) With cgroups you only get access to the devices which are allocated to that cgroup, and you get CUDA_VISIBLE_DEVICES set for you.
Remember also to look at the permissions on /dev/nvidia(0,1,2...) - which are usually OK and on /dev/nvidiactl On Thu, 30 Aug 2018 at 15:52, Renfro, Michael <ren...@tntech.edu> wrote: > Chris’ method will set CUDA_VISIBLE_DEVICES like you’re used to, and it > will help keep you or your users from picking conflicting devices. > > My cgroup/GPU settings from slurm.conf: > > ===== > > [renfro@login ~]$ egrep -i '(cgroup|gpu)' /etc/slurm/slurm.conf | grep -v > '^#' > ProctrackType=proctrack/cgroup > TaskPlugin=task/affinity,task/cgroup > NodeName=gpunode[001-004] CoresPerSocket=14 RealMemory=126000 Sockets=2 > ThreadsPerCore=1 Gres=gpu:2 > PartitionName=gpu Default=NO MinNodes=1 DefaultTime=1-00:00:00 > MaxTime=30-00:00:00 AllowGroups=ALL PriorityJobFactor=1 PriorityTier=1 > DisableRootJobs=NO RootOnly=NO Hidden=NO Shared=NO GraceTime=0 > PreemptMode=OFF ReqResv=NO DefMemPerCPU=4000 AllowAccounts=ALL AllowQos=ALL > LLN=NO ExclusiveUser=NO OverSubscribe=NO OverTimeLimit=0 State=UP > Nodes=gpunode[001-004] > PartitionName=gpu-debug Default=NO MinNodes=1 MaxTime=00:30:00 > AllowGroups=ALL PriorityJobFactor=2 PriorityTier=1 DisableRootJobs=NO > RootOnly=NO Hidden=NO Shared=NO GraceTime=0 PreemptMode=OFF ReqResv=NO > DefMemPerCPU=4000 AllowAccounts=ALL AllowQos=ALL LLN=NO ExclusiveUser=NO > OverSubscribe=NO OverTimeLimit=0 State=UP Nodes=gpunode[001-004] > PartitionName=gpu-interactive Default=NO MinNodes=1 MaxNodes=2 > MaxTime=02:00:00 AllowGroups=ALL PriorityJobFactor=3 PriorityTier=1 > DisableRootJobs=NO RootOnly=NO Hidden=NO Shared=NO GraceTime=0 > PreemptMode=OFF ReqResv=NO DefMemPerCPU=4000 AllowAccounts=ALL AllowQos=ALL > LLN=NO ExclusiveUser=NO OverSubscribe=NO OverTimeLimit=0 State=UP > Nodes=gpunode[001-004] > GresTypes=gpu,mic > > ===== > > Example (where srun is a function that runs “srun --pty $SHELL -I”), with > no CUDA_VISIBLE_DEVICES on the submit host, but is correctly set on > reserving GPUs: > > ===== > > [renfro@login ~]$ echo $CUDA_VISIBLE_DEVICES > > [renfro@login ~]$ hpcshell --partition=gpu-interactive --gres=gpu:1 > [renfro@gpunode003 ~]$ echo $CUDA_VISIBLE_DEVICES > 0 > [renfro@login ~]$ hpcshell --partition=gpu-interactive --gres=gpu:2 > [renfro@gpunode004 ~]$ echo $CUDA_VISIBLE_DEVICES > 0,1 > > ===== > > > On Aug 30, 2018, at 4:18 AM, Chaofeng Zhang <zhang...@lenovo.com> wrote: > > > > CUDA_VISBLE_DEVICES is used by many AI framework to determine which gpu > to use, like tensorflow. So this environment is critical to us. > > > > -----Original Message----- > > From: slurm-users <slurm-users-boun...@lists.schedmd.com> On Behalf Of > Chris Samuel > > Sent: Thursday, August 30, 2018 4:42 PM > > To: slurm-users@lists.schedmd.com > > Subject: [External] Re: [slurm-users] serious bug about > CUDA_VISBLE_DEVICES in the slurm 17.11.7 > > > > On Thursday, 30 August 2018 6:38:08 PM AEST Chaofeng Zhang wrote: > > > >> The CUDA_VISBLE_DEVICES can't be set NoDevFiles in Slurm 17.11.7. > >> This is worked when we use Slurm 17.02. > > > > You probably should be using cgroups instead to constrain access to > GPUs. > > Then it doesn't matter what you set CUDA_VISBLE_DEVICES to be as > processes will only be able to access what they requested. > > > > Hope that helps! > > Chris > > -- > > Chris Samuel : http://www.csamuel.org/ : Melbourne, VIC > > > > > > > > > > > >
