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slurmstepd: error: load_ebpf_prog: BPF load error (No space left on device). Please check your system limits (MEMLOCK).
by Tim Schneider 22 Jan '24

22 Jan '24

Hi, I am using SLURM 22.05.9 on a small compute cluster. Since I reinstalled two of our nodes, I get the following error when launching a job: slurmstepd: error: load_ebpf_prog: BPF load error (No space left on device). Please check your system limits (MEMLOCK). Also the cgroups do not seem to work properly anymore, as I am able to see all GPUs even if I do not request them, which is not the case on the other nodes. One difference I found between the updated nodes and the original nodes (both are Ubuntu 22.04) is the kernel version, which is "5.15.0-89-generic #99-Ubuntu SMP" on the functioning nodes and "5.15.0-91-generic #101-Ubuntu SMP" on the updated nodes. I could not figure out how to install the exact first kernel version on the updated nodes, but I noticed that when I reinstall 5.15.0 with this tool: https://github.com/pimlie/ubuntu-mainline-kernel.sh, the error message disappears. However, once I do that, the network driver does not function properly anymore, so this does not seem to be a good solution. Has anyone seen this issue before or is there maybe something else I should take a look at? I am also happy to just find a workaround such that I can take these nodes back online. I appreciate any help! Thanks a lot in advance and best wishes, Tim

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Tried setting up GANG scheduling for timeslicing, but jobs are not alternating
by Francisco José Letterio 22 Jan '24

22 Jan '24

I'm trying to set up GANG scheduling with Slurm on my single-node server so the people at the lab can run experiments without blocking each other (so if say someone has to run some code that takes days to finish, other jobs that take less have the chance to run alternated with it and so they don't have to wait days until they can run) I followed the GANG scheduling slurm.conf setup tutorial on the slurm website, and to check if it's working properly I launched a bunch of jobs that print the current time and then sleep for a while. But when I check squeue, the jobs never alternate, they run sequentially one after the other. Why is this happening? Here's my slurm.conf file: # See the slurm.conf man page for more information. ClusterName=localcluster SlurmctldHost=localhost ProctrackType=proctrack/linuxproc ReturnToService=1 SlurmctldPidFile=/var/run/slurmctld.pid SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid SlurmdPort=6818 SlurmdSpoolDir=/var/lib/slurm/slurmd SlurmUser=slurm StateSaveLocation=/var/lib/slurm/slurmctld TaskPlugin=task/none # TIMERS InactiveLimit=0 KillWait=30 MinJobAge=300 SlurmctldTimeout=120 SlurmdTimeout=300 Waittime=0 # SCHEDULING #Seteado para que cada laburo alterne cada 15 segundos SchedulerTimeSlice=15 SchedulerType=sched/builtin SelectType=select/linear SelectTypeParameters=CR_Memory PreemptMode=GANG # LOGGING AND ACCOUNTING JobCompType=jobcomp/none JobAcctGatherFrequency=30 SlurmctldDebug=info SlurmctldLogFile=/var/log/slurm/slurmctld.log SlurmdDebug=info SlurmdLogFile=/var/log/slurm/slurmd.log # COMPUTE NODES NodeName=lab04 CPUs=48 CoresPerSocket=12 ThreadsPerCore=2 State=UNKNOWN RealMemory=257249 PartitionName=LocalQ Nodes=ALL Default=YES MaxTime=INFINITE State=UP OverSubscribe=FORCE:6 DefMemPerNode=257249 MaxMemPerNode=257249 >From what I understand, SchedulerTimeSlice=15 means that jobs should alternate running every 15 seconds. This is the job I'm launching with sbatch (launching many copies of this job one after the other): #!/bin/bash #SBATCH -J test # Job name #SBATCH -o job.%j.out # Name of stdout output file (%j expands to %jobId) #SBATCH -N 1 # Total number of nodes requested echo "Test output from Slurm Testjob" date sleep 10 date sleep 10 date sleep 10 date sleep 10 I would expect jobs to print one or two dates, then the Slurm scheduler comes and lets another job run in the meantime, and then the final print(s) come with a delay way greater than 10secs However after launching many copies of this job this is what I see on squeue : Before the first job launched is done: JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 18 LocalQ test eiarussi PD 0:00 1 (Resources) 19 LocalQ test eiarussi PD 0:00 1 (Priority) 20 LocalQ test eiarussi PD 0:00 1 (Priority) 21 LocalQ test eiarussi PD 0:00 1 (Priority) 17 LocalQ test eiarussi R 0:31 1 lab04 After that job ends: JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 19 LocalQ test eiarussi PD 0:00 1 (Resources) 20 LocalQ test eiarussi PD 0:00 1 (Priority) 21 LocalQ test eiarussi PD 0:00 1 (Priority) 18 LocalQ test eiarussi R 0:02 1 lab04 Why did the first job run for 30+ seconds uninterrupted, instead of another job being allowed to run after 15 seconds? Am I misunderstanding how GANG scheduling works, or is the a problem in my conf file?

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MIG-Slice: Unavailable GRES
by Dražen Jalšovec 22 Jan '24

22 Jan '24

Hi, We are testing the MIG deployment on our new slurm compute node with 4 x H100 GPUs. It looks like everything is configured correctly but we have a problem accessing mig devices. When I submit jobs requesting a mig gpu device #SBATCH --gres=gpu:H100_1g.10gb:1, the jobs get submitted to the node, but only 4 jobs get executed and all other jobs fail. I was able to "solve it" by adding code to submit to the mig device UUID. Am I missing something in the slurm/gres/cgroups configuration that would automatically assign correct CUDA_VISIBLE_DEVICES MIG UUID to the job? If I submit 10 jobs requesting --gres=gpu:H100_1g.10gb:1 8 jobs start running: #1 - CUDA_VISIBLE_DEVICES=0 - jobs 1-4 run on one of the mig devices configured on GPU-device ... #8 - CUDA_VISIBLE_DEVICES=7 - jobs 5-8 fail with the message that there are no available CUDA_VISIBLE_DEVICES ... #10 - CUDA_VISIBLE_DEVICES=? - after waiting for free mig resources jobs 9-10 runs or fails depending if GPU-device is free (migs are free) $ nvidia-smi --query-gpu=uuid --id=0 --format=csv,noheader GPU-6c7f34eb-7b0b-9b09-4a2a-a009fb1ba6d3 $ nvidia-smi --query-gpu=uuid --id=7 --format=csv,noheader No devices were found The slurm assigns CUDA_VISIBLE_DEVICES numbers based on the requested resources: Request: gpu:H100_1g.10gb:1 - CUDA_VISIBLE_DEVICES=0 gpu:H100_1g.10gb:2 - CUDA_VISIBLE_DEVICES=0,1 ... Any job with H100_1g.10gb request is going to get device number between 0 and 7 gpu:H100_1g.10gb:8 - CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 gpu:H100_2g.20gb:1 - CUDA_VISIBLE_DEVICES=8 Any job with H100_2g.20gb request is going to get device number between 8 and 10 ... gpu:H100_2g.10gb:3 - CUDA_VISIBLE_DEVICES=8,9,10 gpu:H100_3g.40gb:1 - CUDA_VISIBLE_DEVICES=11 gpu:H100_3g.40gb:2 - CUDA_VISIBLE_DEVICES=12 gpu:H100_7g.80gb:1 - CUDA_VISIBLE_DEVICES=13 I was able to "solve it" by adding this code to my slurm script: # Get the CUDA index using srun and awk CUDA_INDEX=$(srun env | grep CUDA_VISIBLE_DEVICES | awk -F '=' '{print $2}') ## GPU0 1g.10gb ## MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a ## MIG-d1679b4c-c8e7-5d78-bd63-07334ecad664 ## MIG-d3ab0675-d318-5e53-b487-b50695cf2e00 ## MIG-c1384014-7937-5942-ba2a-900bd8a4c4b0 ## MIG-85dd76da-c994-5830-adf0-467c66ae1b95 ## MIG-29a6d43b-882e-5b79-868a-15bb2c770b82 ## MIG-558debd2-dc13-5406-9256-73ef4f279737 ## GPU1 2g.20gb ## MIG-a78cd536-25b7-53cb-941a-a2db5eb0375f ## MIG-0a804c0c-aa27-5993-97cb-cedb854735ce ## MIG-8af9af6d-8720-5763-81e2-83afc43eb42b ## GPU1 1g.10gb ## MIG-68d47441-f5fb-5a3b-ab5b-a0f3449c5b2a ## GPU2 3g.40gb ## MIG-2ed972aa-6640-5116-9271-3d61c0dc9f3b ## MIG-3bdad788-60a5-5bef-85b9-fbdb36dc71fc ## GPU3 7g.80gb ## MIG-ec043869-9176-577e-bac0-46c8411e4e37 # Define the list of UUIDs UUIDS=( "MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a" "MIG-d1679b4c-c8e7-5d78-bd63-07334ecad664" "MIG-d3ab0675-d318-5e53-b487-b50695cf2e00" "MIG-c1384014-7937-5942-ba2a-900bd8a4c4b0" "MIG-85dd76da-c994-5830-adf0-467c66ae1b95" "MIG-29a6d43b-882e-5b79-868a-15bb2c770b82" "MIG-558debd2-dc13-5406-9256-73ef4f279737" "MIG-68d47441-f5fb-5a3b-ab5b-a0f3449c5b2a" "MIG-a78cd536-25b7-53cb-941a-a2db5eb0375f" "MIG-0a804c0c-aa27-5993-97cb-cedb854735ce" "MIG-8af9af6d-8720-5763-81e2-83afc43eb42b" "MIG-2ed972aa-6640-5116-9271-3d61c0dc9f3b" "MIG-3bdad788-60a5-5bef-85b9-fbdb36dc71fc" "MIG-ec043869-9176-577e-bac0-46c8411e4e37" ) # Assign the UUID based on the CUDA index SELECTED_UUID=${UUIDS[$CUDA_INDEX]} # Print the selected UUID echo $SELECTED_UUID export CUDA_VISIBLE_DEVICES=$SELECTED_UUID echo "CUDA_VISIBLE_DEVICES set to: "$SELECTED_UUID echo "Test: "$CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES=$SELECTED_UUID python3.8 gpu_script.py Result: Script start MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a CUDA_VISIBLE_DEVICES set to: MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a Matrix calculation on CUDA device completed successfully. Script end +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | 0 7 0 28686 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 8 0 28744 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 9 0 28300 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 10 0 28512 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 11 0 28506 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 12 0 28516 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 13 0 28511 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 1 9 0 28552 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | +---------------------------------------------------------------------------------------+ This is the current setup: RockyLinux 9.2 slurm-22.05.9-1.el9.x86_64 CUDA Version: 12.1 Driver Version: 530.30.02 # nvidia-smi -L GPU 0: NVIDIA H100 80GB HBM3 (UUID: GPU-6c7f34eb-7b0b-9b09-4a2a-a009fb1ba6d3) MIG 1g.10gb Device 0: (UUID: MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a) MIG 1g.10gb Device 1: (UUID: MIG-d1679b4c-c8e7-5d78-bd63-07334ecad664) MIG 1g.10gb Device 2: (UUID: MIG-d3ab0675-d318-5e53-b487-b50695cf2e00) MIG 1g.10gb Device 3: (UUID: MIG-c1384014-7937-5942-ba2a-900bd8a4c4b0) MIG 1g.10gb Device 4: (UUID: MIG-85dd76da-c994-5830-adf0-467c66ae1b95) MIG 1g.10gb Device 5: (UUID: MIG-29a6d43b-882e-5b79-868a-15bb2c770b82) MIG 1g.10gb Device 6: (UUID: MIG-558debd2-dc13-5406-9256-73ef4f279737) GPU 1: NVIDIA H100 80GB HBM3 (UUID: GPU-1439a39c-948b-0657-98e9-aff8595a8729) MIG 2g.20gb Device 0: (UUID: MIG-a78cd536-25b7-53cb-941a-a2db5eb0375f) MIG 2g.20gb Device 1: (UUID: MIG-0a804c0c-aa27-5993-97cb-cedb854735ce) MIG 2g.20gb Device 2: (UUID: MIG-8af9af6d-8720-5763-81e2-83afc43eb42b) MIG 1g.10gb Device 3: (UUID: MIG-68d47441-f5fb-5a3b-ab5b-a0f3449c5b2a) GPU 2: NVIDIA H100 80GB HBM3 (UUID: GPU-3b182fed-bfdf-bc11-d1bd-c41fd1468d2a) MIG 3g.40gb Device 0: (UUID: MIG-2ed972aa-6640-5116-9271-3d61c0dc9f3b) MIG 3g.40gb Device 1: (UUID: MIG-3bdad788-60a5-5bef-85b9-fbdb36dc71fc) GPU 3: NVIDIA H100 80GB HBM3 (UUID: GPU-7eba9d78-e908-3db8-2633-a269aeec395e) MIG 7g.80gb Device 0: (UUID: MIG-ec043869-9176-577e-bac0-46c8411e4e37) $ scontrol show node node16 NodeName=node16 Arch=x86_64 CoresPerSocket=1 CPUAlloc=1 CPUEfctv=112 CPUTot=112 CPULoad=0.00 AvailableFeatures=(null) ActiveFeatures=(null) Gres=gpu:H100_1g.10gb:8,gpu:H100_2g.20gb:3,gpu:H100_3g.40gb:2,gpu:H100_7g.80gb:1 NodeAddr=node16 NodeHostName=node16 Version=22.05.9 OS=Linux 5.14.0-284.30.1.el9_2.x86_64 #1 SMP PREEMPT_DYNAMIC Sat Sep 16 09:55:41 UTC 2023 RealMemory=1030000 AllocMem=0 FreeMem=1011801 Sockets=112 Boards=1 State=MIXED ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A Partitions=gpu_H100 BootTime=2024-01-18T13:52:27 SlurmdStartTime=2024-01-19T11:25:44 LastBusyTime=2024-01-19T10:59:13 CfgTRES=cpu=112,mem=1030000M,billing=112,gres/gpu=14 AllocTRES=cpu=1 CapWatts=n/a CurrentWatts=0 AveWatts=0 ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s $ scontrol show partition gpu_H100 PartitionName=gpu_H100 AllowGroups=ALL AllowAccounts=ALL AllowQos=ALL AllocNodes=ALL Default=NO QoS=N/A DefaultTime=NONE DisableRootJobs=NO ExclusiveUser=NO GraceTime=0 Hidden=NO MaxNodes=UNLIMITED MaxTime=UNLIMITED MinNodes=0 LLN=NO MaxCPUsPerNode=UNLIMITED Nodes=node16 PriorityJobFactor=1 PriorityTier=1 RootOnly=NO ReqResv=NO OverSubscribe=NO OverTimeLimit=NONE PreemptMode=OFF State=UP TotalCPUs=112 TotalNodes=1 SelectTypeParameters=NONE JobDefaults=(null) DefMemPerNode=UNLIMITED MaxMemPerNode=UNLIMITED TRES=cpu=112,mem=1030000M,node=1,billing=112,gres/gpu=14 slurm.conf: AccountingStorageTRES=gres/gpu,gres/gpu:A100,gres/gpu:H100,gres/gpu:H100_1g.10gb,gres/gpu:H100_2g.20gb,gres/gpu:H100_3g.40gb,gres/gpu:H100_7g.80gb GresTypes=gpu ... NodeName=node16 CPUs=112 RealMemory=1030000 Gres=gpu:H100_1g.10gb:8,gpu:H100_2g.20gb:3,gpu:H100_3g.40gb:2,gpu:H100_7g.80gb:1 PartitionName=gpu_H100 Nodes=node16 Default=NO MaxTime=INFINITE State=UP gres.conf #AutoDetect=nvml # GPU 0 MIG 0 /proc/driver/nvidia/capabilities/gpu0/mig/gi7/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap66,/dev/nvidia-caps/nvidia-cap67 CPUs=0-55 # GPU 0 MIG 1 /proc/driver/nvidia/capabilities/gpu0/mig/gi8/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap75,/dev/nvidia-caps/nvidia-cap76 CPUs=0-55 # GPU 0 MIG 2 /proc/driver/nvidia/capabilities/gpu0/mig/gi9/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap84,/dev/nvidia-caps/nvidia-cap85 CPUs=0-55 # GPU 0 MIG 3 /proc/driver/nvidia/capabilities/gpu0/mig/gi10/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap93,/dev/nvidia-caps/nvidia-cap94 CPUs=0-55 # GPU 0 MIG 4 /proc/driver/nvidia/capabilities/gpu0/mig/gi11/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap102,/dev/nvidia-caps/nvidia-cap103 CPUs=0-55 # GPU 0 MIG 5 /proc/driver/nvidia/capabilities/gpu0/mig/gi12/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap111,/dev/nvidia-caps/nvidia-cap112 CPUs=0-55 # GPU 0 MIG 6 /proc/driver/nvidia/capabilities/gpu0/mig/gi13/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap120,/dev/nvidia-caps/nvidia-cap121 CPUs=0-55 # GPU 1 MIG 0 /proc/driver/nvidia/capabilities/gpu1/mig/gi3/access NodeName=node16 Name=gpu Type=H100_2g.20gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap165,/dev/nvidia-caps/nvidia-cap166 CPUs=0-55 # GPU 1 MIG 1 /proc/driver/nvidia/capabilities/gpu1/mig/gi5/access NodeName=node16 Name=gpu Type=H100_2g.20gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap183,/dev/nvidia-caps/nvidia-cap184 CPUs=0-55 # GPU 1 MIG 2 /proc/driver/nvidia/capabilities/gpu1/mig/gi6/access NodeName=node16 Name=gpu Type=H100_2g.20gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap192,/dev/nvidia-caps/nvidia-cap193 CPUs=0-55 # GPU 1 MIG 3 /proc/driver/nvidia/capabilities/gpu1/mig/gi9/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap219,/dev/nvidia-caps/nvidia-cap220 CPUs=0-55 # GPU 2 MIG 0 /proc/driver/nvidia/capabilities/gpu2/mig/gi1/access NodeName=node16 Name=gpu Type=H100_3g.40gb MultipleFiles=/dev/nvidia2,/dev/nvidia-caps/nvidia-cap282,/dev/nvidia-caps/nvidia-cap283 CPUs=0-55 # GPU 2 MIG 1 /proc/driver/nvidia/capabilities/gpu2/mig/gi2/access NodeName=node16 Name=gpu Type=H100_3g.40gb MultipleFiles=/dev/nvidia2,/dev/nvidia-caps/nvidia-cap291,/dev/nvidia-caps/nvidia-cap292 CPUs=0-55 # GPU 3 MIG 0 /proc/driver/nvidia/capabilities/gpu3/mig/gi0/access NodeName=node16 Name=gpu Type=H100_7g.80gb MultipleFiles=/dev/nvidia3,/dev/nvidia-caps/nvidia-cap408,/dev/nvidia-caps/nvidia-cap409 CPUs=0-55 [2024-01-19T14:31:13.701] debug: gres/gpu: init: loaded [2024-01-19T14:31:13.701] debug: gpu/generic: init: init: GPU Generic plugin loaded [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_2g.20gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_2g.20gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_2g.20gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_3g.40gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_3g.40gb Count=1 [2024-01-19T14:31:13.702] Gres Name=gpu Type=H100_7g.80gb Count=1 Best, Drazen Jalsovec

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Is there a way to map Azure AD users to NIS?
by Ben Wellborn 20 Jan '24

20 Jan '24

In the past, we would connect AD to NIS users using Centrify and adnisd, but I don't think adnisd can connect to Azure AD (HCI stack). Thanks, Ben

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Re: [slurm-users] Need help with running multiple instances/executions of a batch script in parallel (with NVIDIA HGX A100 GPU as a Gres)
by Jason Macklin 19 Jan '24

19 Jan '24

If you run "scontrol show jobid <jobid>" of your pending job with the "(Resources)" tag you may see more about what is unavailable to your job. Slurm default configs can cause an entire compute node of resources to be "allocated" to a running job regardless of whether it needs all of them or not so you may need to alter one or both of the following settings to allow more than one job to run on a single node at once. You'll find these in your slurm.conf. Don't forget to "scontrol reconf" and even potentially restart both "slurmctld" & "slurmd" on your nodes if you do end up making changes. SelectType=select/cons_tres SelectTypeParameters=CR_Core_Memory I hope this helps. Kind regards, Jason ---- Jason Macklin Manager Cyberinfrastructure, Research Cyberinfrastructure 860.837.2142 t | 860.202.7779 m jason.macklin(a)jax.org The Jackson Laboratory Maine | Connecticut | California | Shanghai www.jax.org<http://www.jax.org> The Jackson Laboratory: Leading the search for tomorrow's cures ________________________________ From: slurm-users <slurm-users-bounces(a)lists.schedmd.com> on behalf of slurm-users-request(a)lists.schedmd.com <slurm-users-request(a)lists.schedmd.com> Sent: Friday, January 19, 2024 9:24 AM To: slurm-users(a)lists.schedmd.com <slurm-users(a)lists.schedmd.com> Subject: [EXTERNAL]slurm-users Digest, Vol 75, Issue 31 Send slurm-users mailing list submissions to slurm-users(a)lists.schedmd.com To subscribe or unsubscribe via the World Wide Web, visit https://lists.schedmd.com/cgi-bin/mailman/listinfo/slurm-users or, via email, send a message with subject or body 'help' to slurm-users-request(a)lists.schedmd.com You can reach the person managing the list at slurm-users-owner(a)lists.schedmd.com When replying, please edit your Subject line so it is more specific than "Re: Contents of slurm-users digest..." Today's Topics: 1. Re: Need help with running multiple instances/executions of a batch script in parallel (with NVIDIA HGX A100 GPU as a Gres) (Marko Markoc) 2. Re: Need help with running multiple instances/executions of a batch script in parallel (with NVIDIA HGX A100 GPU as a Gres) (?mit Seren) ---------------------------------------------------------------------- Message: 1 Date: Fri, 19 Jan 2024 06:12:24 -0800 From: Marko Markoc <mmarkoc(a)pdx.edu> To: Slurm User Community List <slurm-users(a)lists.schedmd.com> Subject: Re: [slurm-users] Need help with running multiple instances/executions of a batch script in parallel (with NVIDIA HGX A100 GPU as a Gres) Message-ID: <CABnuMe4JTA0e6=VbO8D+To=8FGO+3Byv1dK_MC+OuRitzN5dXg(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" +1 on checking the memory allocation. Or add/check if you have any DefMemPerX set in your slurm.conf On Fri, Jan 19, 2024 at 12:33?AM mohammed shambakey <shambakey1(a)gmail.com> wrote: > Hi > > I'm not an expert, but is it possible that the currently running jobs is > consuming the whole node because it is allocated the whole memory of the > node (so the other 2 jobs had to wait until it finishes)? > Maybe if you try to restrict the required memory for each job? > > Regards > > On Thu, Jan 18, 2024 at 4:46?PM ?mit Seren <uemit.seren(a)gmail.com> wrote: > >> This line also has tobe changed: >> >> >> #SBATCH --gpus-per-node=4 ? #SBATCH --gpus-per-node=1 >> >> --gpus-per-node seems to be the new parameter that is replacing the --gres= >> one, so you can remove the ?gres line completely. >> >> >> >> Best >> >> ?mit >> >> >> >> *From: *slurm-users <slurm-users-bounces(a)lists.schedmd.com> on behalf of >> Kherfani, Hafedh (Professional Services, TC) <hafedh.kherfani(a)hpe.com> >> *Date: *Thursday, 18. January 2024 at 15:40 >> *To: *Slurm User Community List <slurm-users(a)lists.schedmd.com> >> *Subject: *Re: [slurm-users] Need help with running multiple >> instances/executions of a batch script in parallel (with NVIDIA HGX A100 >> GPU as a Gres) >> >> Hi Noam and Matthias, >> >> >> >> Thanks both for your answers. >> >> >> >> I changed the ?#SBATCH --gres=gpu:4? directive (in the batch script) with >> ?#SBATCH --gres=gpu:1? as you suggested, but it didn?t make a difference, >> as running this batch script 3 times will result in the first job to be in >> a running state, while the second and third jobs will still be in a pending >> state ? >> >> >> >> [slurmtest@c-a100-master test-batch-scripts]$ cat gpu-job.sh >> >> #!/bin/bash >> >> #SBATCH --job-name=gpu-job >> >> #SBATCH --partition=gpu >> >> #SBATCH --nodes=1 >> >> #SBATCH --gpus-per-node=4 >> >> #SBATCH --gres=gpu:1 # <<<< Changed from ?4? >> to ?1? >> >> #SBATCH --tasks-per-node=1 >> >> #SBATCH --output=gpu_job_output.%j >> >> #SBATCH --error=gpu_job_error.%j >> >> >> >> hostname >> >> date >> >> sleep 40 >> >> pwd >> >> >> >> [slurmtest@c-a100-master test-batch-scripts]$ sbatch gpu-job.sh >> >> Submitted batch job *217* >> >> [slurmtest@c-a100-master test-batch-scripts]$ squeue >> >> JOBID PARTITION NAME USER ST TIME NODES >> NODELIST(REASON) >> >> 217 gpu gpu-job slurmtes R 0:02 1 >> c-a100-cn01 >> >> [slurmtest@c-a100-master test-batch-scripts]$ sbatch gpu-job.sh >> >> Submitted batch job *218* >> >> [slurmtest@c-a100-master test-batch-scripts]$ sbatch gpu-job.sh >> >> Submitted batch job *219* >> >> [slurmtest@c-a100-master test-batch-scripts]$ squeue >> >> JOBID PARTITION NAME USER ST TIME NODES >> NODELIST(REASON) >> >> 219 gpu gpu-job slurmtes *PD* 0:00 1 >> (Priority) >> >> 218 gpu gpu-job slurmtes *PD* 0:00 1 >> (Resources) >> >> 217 gpu gpu-job slurmtes *R* 0:07 1 >> c-a100-cn01 >> >> >> >> Basically I?m seeking for some help/hints on how to tell Slurm, from the >> batch script for example: ?I want only 1 or 2 GPUs to be used/consumed by >> the job?, and then I run the batch script/job a couple of times with sbatch >> command, and confirm that we can indeed have multiple jobs using a GPU and >> running in parallel, at the same time. >> >> >> >> Makes sense ? >> >> >> >> >> >> Best regards, >> >> >> >> *Hafedh * >> >> >> >> *From:* slurm-users <slurm-users-bounces(a)lists.schedmd.com> *On Behalf >> Of *Bernstein, Noam CIV USN NRL (6393) Washington DC (USA) >> *Sent:* jeudi 18 janvier 2024 2:30 PM >> *To:* Slurm User Community List <slurm-users(a)lists.schedmd.com> >> *Subject:* Re: [slurm-users] Need help with running multiple >> instances/executions of a batch script in parallel (with NVIDIA HGX A100 >> GPU as a Gres) >> >> >> >> On Jan 18, 2024, at 7:31 AM, Matthias Loose <m.loose(a)mindcode.de> wrote: >> >> >> >> Hi Hafedh, >> >> Im no expert in the GPU side of SLURM, but looking at you current >> configuration to me its working as intended at the moment. You have defined >> 4 GPUs and start multiple jobs each consuming 4 GPUs each. So the jobs wait >> for the ressource the be free again. >> >> I think what you need to look into is the MPS plugin, which seems to do >> what you are trying to achieve: >> https://slurm.schedmd.com/gres.html#MPS_Management >> >> >> >> I agree with the first paragraph. How many GPUs are you expecting each >> job to use? I'd have assumed, based on the original text, that each job is >> supposed to use 1 GPU, and the 4 jobs were supposed to be running >> side-by-side on the one node you have (with 4 GPUs). If so, you need to >> tell each job to request only 1 GPU, and currently each one is requesting 4. >> >> >> >> If your jobs are actually supposed to be using 4 GPUs each, I still don't >> see any advantage to MPS (at least in what is my usual GPU usage pattern): >> all the jobs will take longer to finish, because they are sharing the fixed >> resource. If they take turns, at least the first ones finish as fast as >> they can, and the last one will finish no later than it would have if they >> were all time-sharing the GPUs. I guess NVIDIA had something in mind when >> they developed MPS, so I guess our pattern may not be typical (or at least >> not universal), and in that case the MPS plugin may well be what you need. >> > > > -- > Mohammed >

1 0

slurmctld/slurmdbd (code=exited, status=217/USER)
by Miriam Olmi 19 Jan '24

19 Jan '24

Hi all, I am having some issue with the new version of slurm 23.11.0-1. I had already installed and configured slurm 23.02.3-1 on my cluster and all the services were active and running properly. After I install with the same procedure the new version of slurm I have that the slurmctld and slurmdbd daemons fail to start all with the same error: (code=exited, status=217/USER) And investigating the problem with the command journalctl -xe I find: slurmctld.service: Failed to determine user credentials: No such process slurmctld.service: Failed at step USER spawning /usr/sbin/slurmctld: No such process I had a look at the slurmctld.service file for both the slurm versions and I found the following differences in the [Service] section. >From the slurmctld.service file of slurm 23.02.3-1: [Service] Type=simple EnvironmentFile=-/etc/sysconfig/slurmctld EnvironmentFile=-/etc/default/slurmctld ExecStart=/usr/sbin/slurmctld -D -s $SLURMCTLD_OPTIONS ExecReload=/bin/kill -HUP $MAINPID LimitNOFILE=65536 TasksMax=infinity >From the slurmctld.service file of slurm 23.11.0-1: [Service] Type=notify EnvironmentFile=-/etc/sysconfig/slurmctld EnvironmentFile=-/etc/default/slurmctld User=slurm Group=slurm ExecStart=/usr/sbin/slurmctld --systemd $SLURMCTLD_OPTIONS ExecReload=/bin/kill -HUP $MAINPID LimitNOFILE=65536 TasksMax=infinity I think the presence of the new lines regarding the slurm user might be the problem but I am not sure and I have no idea how to solve it. Can anyone halp me? Thanks in advance, Miriam

2 1

Jobs exiting together
by Alexander Silva 19 Jan '24

19 Jan '24

Recently, i have built an hpc cluster with slurm as workload. The test jobs with quatum chemistry codes have worked fine. However, production jobs with lammps have shown an unexpected behavior when the first job completed, normally or not, cause the termination of the others in the same compute node. Initially, I thought that was due to mpi malfunction, but this behavior is algo observed for serial lammps code. The lammps group said to me that behavior could be generated by slurm. My question to you is about what parameter in slurm.conf could be responsible for the termination of the other jobs. I am using an epilogue script that work normally in another cluster. Thanks.

1 0

Running slurm job on requested nvidia mig device
by Dražen Jalšovec 19 Jan '24

19 Jan '24

Hi, We are testing the MIG deployment on our new slurm compute node with 4 x H100 GPUs. It looks like everything is configured correctly but we have a problem accessing mig devices. When I submit jobs requesting a mig gpu device #SBATCH --gres=gpu:H100_1g.10gb:1, the jobs get submitted to the node, but only 4 jobs get executed and all other jobs fail. I was able to "solve it" by adding code to submit to the mig device UUID. Am I missing something in the slurm/gres/cgroups configuration that would automatically assign correct CUDA_VISIBLE_DEVICES MIG UUID to the job? If I submit 10 jobs requesting --gres=gpu:H100_1g.10gb:1 8 jobs start running: #1 - CUDA_VISIBLE_DEVICES=0 - jobs 1-4 run on one of the mig devices configured on GPU-device ... #8 - CUDA_VISIBLE_DEVICES=7 - jobs 5-8 fail with the message that there are no available CUDA_VISIBLE_DEVICES ... #10 - CUDA_VISIBLE_DEVICES=? - after waiting for free mig resources jobs 9-10 runs or fails depending if GPU-device is free (migs are free) $ nvidia-smi --query-gpu=uuid --id=0 --format=csv,noheader GPU-6c7f34eb-7b0b-9b09-4a2a-a009fb1ba6d3 $ nvidia-smi --query-gpu=uuid --id=7 --format=csv,noheader No devices were found The slurm assigns CUDA_VISIBLE_DEVICES numbers based on the requested resources: Request: gpu:H100_1g.10gb:1 - CUDA_VISIBLE_DEVICES=0 gpu:H100_1g.10gb:2 - CUDA_VISIBLE_DEVICES=0,1 ... Any job with H100_1g.10gb request is going to get device number between 0 and 7 gpu:H100_1g.10gb:8 - CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 gpu:H100_2g.20gb:1 - CUDA_VISIBLE_DEVICES=8 Any job with H100_2g.20gb request is going to get device number between 8 and 10 ... gpu:H100_2g.10gb:3 - CUDA_VISIBLE_DEVICES=8,9,10 gpu:H100_3g.40gb:1 - CUDA_VISIBLE_DEVICES=11 gpu:H100_3g.40gb:2 - CUDA_VISIBLE_DEVICES=12 gpu:H100_7g.80gb:1 - CUDA_VISIBLE_DEVICES=13 I was able to "solve it" by adding this code to my slurm script: # Get the CUDA index using srun and awk CUDA_INDEX=$(srun env | grep CUDA_VISIBLE_DEVICES | awk -F '=' '{print $2}') ## GPU0 1g.10gb ## MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a ## MIG-d1679b4c-c8e7-5d78-bd63-07334ecad664 ## MIG-d3ab0675-d318-5e53-b487-b50695cf2e00 ## MIG-c1384014-7937-5942-ba2a-900bd8a4c4b0 ## MIG-85dd76da-c994-5830-adf0-467c66ae1b95 ## MIG-29a6d43b-882e-5b79-868a-15bb2c770b82 ## MIG-558debd2-dc13-5406-9256-73ef4f279737 ## GPU1 2g.20gb ## MIG-a78cd536-25b7-53cb-941a-a2db5eb0375f ## MIG-0a804c0c-aa27-5993-97cb-cedb854735ce ## MIG-8af9af6d-8720-5763-81e2-83afc43eb42b ## GPU1 1g.10gb ## MIG-68d47441-f5fb-5a3b-ab5b-a0f3449c5b2a ## GPU2 3g.40gb ## MIG-2ed972aa-6640-5116-9271-3d61c0dc9f3b ## MIG-3bdad788-60a5-5bef-85b9-fbdb36dc71fc ## GPU3 7g.80gb ## MIG-ec043869-9176-577e-bac0-46c8411e4e37 # Define the list of UUIDs UUIDS=( "MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a" "MIG-d1679b4c-c8e7-5d78-bd63-07334ecad664" "MIG-d3ab0675-d318-5e53-b487-b50695cf2e00" "MIG-c1384014-7937-5942-ba2a-900bd8a4c4b0" "MIG-85dd76da-c994-5830-adf0-467c66ae1b95" "MIG-29a6d43b-882e-5b79-868a-15bb2c770b82" "MIG-558debd2-dc13-5406-9256-73ef4f279737" "MIG-68d47441-f5fb-5a3b-ab5b-a0f3449c5b2a" "MIG-a78cd536-25b7-53cb-941a-a2db5eb0375f" "MIG-0a804c0c-aa27-5993-97cb-cedb854735ce" "MIG-8af9af6d-8720-5763-81e2-83afc43eb42b" "MIG-2ed972aa-6640-5116-9271-3d61c0dc9f3b" "MIG-3bdad788-60a5-5bef-85b9-fbdb36dc71fc" "MIG-ec043869-9176-577e-bac0-46c8411e4e37" ) # Assign the UUID based on the CUDA index SELECTED_UUID=${UUIDS[$CUDA_INDEX]} # Print the selected UUID echo $SELECTED_UUID export CUDA_VISIBLE_DEVICES=$SELECTED_UUID echo "CUDA_VISIBLE_DEVICES set to: "$SELECTED_UUID echo "Test: "$CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES=$SELECTED_UUID python3.8 gpu_script.py Result: Script start MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a CUDA_VISIBLE_DEVICES set to: MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a Matrix calculation on CUDA device completed successfully. Script end +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | 0 7 0 28686 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 8 0 28744 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 9 0 28300 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 10 0 28512 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 11 0 28506 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 12 0 28516 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 0 13 0 28511 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | | 1 9 0 28552 C ...gpu-cuda12.1-python38/bin/python3.8 866MiB | +---------------------------------------------------------------------------------------+ This is the current setup: RockyLinux 9.2 slurm-22.05.9-1.el9.x86_64 CUDA Version: 12.1 Driver Version: 530.30.02 # nvidia-smi -L GPU 0: NVIDIA H100 80GB HBM3 (UUID: GPU-6c7f34eb-7b0b-9b09-4a2a-a009fb1ba6d3) MIG 1g.10gb Device 0: (UUID: MIG-fbb82c87-989b-5e2c-98e5-a57919c5cb1a) MIG 1g.10gb Device 1: (UUID: MIG-d1679b4c-c8e7-5d78-bd63-07334ecad664) MIG 1g.10gb Device 2: (UUID: MIG-d3ab0675-d318-5e53-b487-b50695cf2e00) MIG 1g.10gb Device 3: (UUID: MIG-c1384014-7937-5942-ba2a-900bd8a4c4b0) MIG 1g.10gb Device 4: (UUID: MIG-85dd76da-c994-5830-adf0-467c66ae1b95) MIG 1g.10gb Device 5: (UUID: MIG-29a6d43b-882e-5b79-868a-15bb2c770b82) MIG 1g.10gb Device 6: (UUID: MIG-558debd2-dc13-5406-9256-73ef4f279737) GPU 1: NVIDIA H100 80GB HBM3 (UUID: GPU-1439a39c-948b-0657-98e9-aff8595a8729) MIG 2g.20gb Device 0: (UUID: MIG-a78cd536-25b7-53cb-941a-a2db5eb0375f) MIG 2g.20gb Device 1: (UUID: MIG-0a804c0c-aa27-5993-97cb-cedb854735ce) MIG 2g.20gb Device 2: (UUID: MIG-8af9af6d-8720-5763-81e2-83afc43eb42b) MIG 1g.10gb Device 3: (UUID: MIG-68d47441-f5fb-5a3b-ab5b-a0f3449c5b2a) GPU 2: NVIDIA H100 80GB HBM3 (UUID: GPU-3b182fed-bfdf-bc11-d1bd-c41fd1468d2a) MIG 3g.40gb Device 0: (UUID: MIG-2ed972aa-6640-5116-9271-3d61c0dc9f3b) MIG 3g.40gb Device 1: (UUID: MIG-3bdad788-60a5-5bef-85b9-fbdb36dc71fc) GPU 3: NVIDIA H100 80GB HBM3 (UUID: GPU-7eba9d78-e908-3db8-2633-a269aeec395e) MIG 7g.80gb Device 0: (UUID: MIG-ec043869-9176-577e-bac0-46c8411e4e37) $ scontrol show node node16 NodeName=node16 Arch=x86_64 CoresPerSocket=1 CPUAlloc=1 CPUEfctv=112 CPUTot=112 CPULoad=0.00 AvailableFeatures=(null) ActiveFeatures=(null) Gres=gpu:H100_1g.10gb:8,gpu:H100_2g.20gb:3,gpu:H100_3g.40gb:2,gpu:H100_7g.80gb:1 NodeAddr=node16 NodeHostName=node16 Version=22.05.9 OS=Linux 5.14.0-284.30.1.el9_2.x86_64 #1 SMP PREEMPT_DYNAMIC Sat Sep 16 09:55:41 UTC 2023 RealMemory=1030000 AllocMem=0 FreeMem=1011801 Sockets=112 Boards=1 State=MIXED ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A Partitions=gpu_H100 BootTime=2024-01-18T13:52:27 SlurmdStartTime=2024-01-19T11:25:44 LastBusyTime=2024-01-19T10:59:13 CfgTRES=cpu=112,mem=1030000M,billing=112,gres/gpu=14 AllocTRES=cpu=1 CapWatts=n/a CurrentWatts=0 AveWatts=0 ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s $ scontrol show partition gpu_H100 PartitionName=gpu_H100 AllowGroups=ALL AllowAccounts=ALL AllowQos=ALL AllocNodes=ALL Default=NO QoS=N/A DefaultTime=NONE DisableRootJobs=NO ExclusiveUser=NO GraceTime=0 Hidden=NO MaxNodes=UNLIMITED MaxTime=UNLIMITED MinNodes=0 LLN=NO MaxCPUsPerNode=UNLIMITED Nodes=node16 PriorityJobFactor=1 PriorityTier=1 RootOnly=NO ReqResv=NO OverSubscribe=NO OverTimeLimit=NONE PreemptMode=OFF State=UP TotalCPUs=112 TotalNodes=1 SelectTypeParameters=NONE JobDefaults=(null) DefMemPerNode=UNLIMITED MaxMemPerNode=UNLIMITED TRES=cpu=112,mem=1030000M,node=1,billing=112,gres/gpu=14 slurm.conf: AccountingStorageTRES=gres/gpu,gres/gpu:A100,gres/gpu:H100,gres/gpu:H100_1g.10gb,gres/gpu:H100_2g.20gb,gres/gpu:H100_3g.40gb,gres/gpu:H100_7g.80gb GresTypes=gpu ... NodeName=node16 CPUs=112 RealMemory=1030000 Gres=gpu:H100_1g.10gb:8,gpu:H100_2g.20gb:3,gpu:H100_3g.40gb:2,gpu:H100_7g.80gb:1 PartitionName=gpu_H100 Nodes=node16 Default=NO MaxTime=INFINITE State=UP gres.conf #AutoDetect=nvml # GPU 0 MIG 0 /proc/driver/nvidia/capabilities/gpu0/mig/gi7/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap66,/dev/nvidia-caps/nvidia-cap67 CPUs=0-55 # GPU 0 MIG 1 /proc/driver/nvidia/capabilities/gpu0/mig/gi8/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap75,/dev/nvidia-caps/nvidia-cap76 CPUs=0-55 # GPU 0 MIG 2 /proc/driver/nvidia/capabilities/gpu0/mig/gi9/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap84,/dev/nvidia-caps/nvidia-cap85 CPUs=0-55 # GPU 0 MIG 3 /proc/driver/nvidia/capabilities/gpu0/mig/gi10/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap93,/dev/nvidia-caps/nvidia-cap94 CPUs=0-55 # GPU 0 MIG 4 /proc/driver/nvidia/capabilities/gpu0/mig/gi11/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap102,/dev/nvidia-caps/nvidia-cap103 CPUs=0-55 # GPU 0 MIG 5 /proc/driver/nvidia/capabilities/gpu0/mig/gi12/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap111,/dev/nvidia-caps/nvidia-cap112 CPUs=0-55 # GPU 0 MIG 6 /proc/driver/nvidia/capabilities/gpu0/mig/gi13/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia0,/dev/nvidia-caps/nvidia-cap120,/dev/nvidia-caps/nvidia-cap121 CPUs=0-55 # GPU 1 MIG 0 /proc/driver/nvidia/capabilities/gpu1/mig/gi3/access NodeName=node16 Name=gpu Type=H100_2g.20gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap165,/dev/nvidia-caps/nvidia-cap166 CPUs=0-55 # GPU 1 MIG 1 /proc/driver/nvidia/capabilities/gpu1/mig/gi5/access NodeName=node16 Name=gpu Type=H100_2g.20gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap183,/dev/nvidia-caps/nvidia-cap184 CPUs=0-55 # GPU 1 MIG 2 /proc/driver/nvidia/capabilities/gpu1/mig/gi6/access NodeName=node16 Name=gpu Type=H100_2g.20gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap192,/dev/nvidia-caps/nvidia-cap193 CPUs=0-55 # GPU 1 MIG 3 /proc/driver/nvidia/capabilities/gpu1/mig/gi9/access NodeName=node16 Name=gpu Type=H100_1g.10gb MultipleFiles=/dev/nvidia1,/dev/nvidia-caps/nvidia-cap219,/dev/nvidia-caps/nvidia-cap220 CPUs=0-55 # GPU 2 MIG 0 /proc/driver/nvidia/capabilities/gpu2/mig/gi1/access NodeName=node16 Name=gpu Type=H100_3g.40gb MultipleFiles=/dev/nvidia2,/dev/nvidia-caps/nvidia-cap282,/dev/nvidia-caps/nvidia-cap283 CPUs=0-55 # GPU 2 MIG 1 /proc/driver/nvidia/capabilities/gpu2/mig/gi2/access NodeName=node16 Name=gpu Type=H100_3g.40gb MultipleFiles=/dev/nvidia2,/dev/nvidia-caps/nvidia-cap291,/dev/nvidia-caps/nvidia-cap292 CPUs=0-55 # GPU 3 MIG 0 /proc/driver/nvidia/capabilities/gpu3/mig/gi0/access NodeName=node16 Name=gpu Type=H100_7g.80gb MultipleFiles=/dev/nvidia3,/dev/nvidia-caps/nvidia-cap408,/dev/nvidia-caps/nvidia-cap409 CPUs=0-55 [2024-01-19T14:31:13.701] debug: gres/gpu: init: loaded [2024-01-19T14:31:13.701] debug: gpu/generic: init: init: GPU Generic plugin loaded [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_1g.10gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_2g.20gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_2g.20gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_2g.20gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_3g.40gb Count=1 [2024-01-19T14:31:13.701] Gres Name=gpu Type=H100_3g.40gb Count=1 [2024-01-19T14:31:13.702] Gres Name=gpu Type=H100_7g.80gb Count=1 Best, Drazen Jalsovec

1 0

Potential Side Effects of larger MessageTimeout value
by Herc Silverstein 18 Jan '24

18 Jan '24

Hi, What are potential bad side effects of using a large/larger MessageTimeout? And is there a value at which this setting is too large (long)? Thanks, Herc

1 0

error
by Felix 18 Jan '24

18 Jan '24

Hello I started a new AMD node, and the error is as follows: "CPU frequency setting not configured for this node" extended looks like this: [2024-01-18T18:28:06.682] CPU frequency setting not configured for this node [2024-01-18T18:28:06.691] slurmd started on Thu, 18 Jan 2024 18:28:06 +0200 [2024-01-18T18:28:06.691] CPUs=128 Boards=1 Sockets=1 Cores=64 Threads=2 Memory=256786 TmpDisk=875797 Uptime=4569 CPUSpecList=(null) FeaturesAvail=(null) FeaturesActive=(null) In the configuration file I have the following: NodeName=awn-1[04] NodeAddr=192.168.4.[111] CPUs=128 RealMemory=256000 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 Feature=HyperThread Could you please help me? Thank you Felix -- Dr. Eng. Farcas Felix National Institute of Research and Development of Isotopic and Molecular Technology, IT - Department - Cluj-Napoca, Romania Mobile: +40742195323

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2024