May 2024 - slurm-users - lists.schedmd.com

Which "oci.conf" to use?
by Arnuld 14 May '24

14 May '24

I have installed slurm and podman. I have replaced podman's default runtime as per the documentation to "slurm". Documentation says I need to choose one oci.conf: https://slurm.schedmd.com/containers.html#example Which one should I use? runc? crun? nvidia?

1 0

any way to allow interactive jobs or ssh in Slurm 23.02 when node is draining?
by Robert Kudyba 13 May '24

13 May '24

We use Bright Cluster Manager with SLurm 23.02 on RHEL9. I know about pam_slurm_adopt https://slurm.schedmd.com/pam_slurm_adopt.html which does not appear to come by default with the Bright 'cm' package of Slurm. Currently ssh to a node gets: Login not allowed: no running jobs and no WLM allocations We have 8 GPUs on a node so when we drain a node, which can have up to a 5 day job, no new jobs can run. And since we have 20+ TB (yes TB) local drives, researchers have their work and files on them to retrieve. Is there a way to use /etc/security/access.conf to work around this at least temporarily until the reboot and then we can revert? Thanks! Rob

2 2

Announcing Slurm-web v3.0.0, open source web dashboard for Slurm
by Rémi Palancher 13 May '24

13 May '24

Hello Slurm users, Some of you may find interest in the new major version of Slurm-web v3.0.0, an open source web dashboard for Slurm: https://slurm-web.com Slurm-web provides a reactive & responsive web interface to track jobs with intuitive insights and advanced visualizations to monitor status of HPC supercomputers in your organization. The software is released under GPLv3 [1]. This new version is based on official Slurm REST API slurmrestd and adopts modern web technologies to provide many features: - Instant jobs filtering and sorting - Live jobs status update - Advanced visualization of node status with racking topology - Intuitive visualization of QOS and advanced reservations - Multi-clusters support - LDAP authentication - Advanced RBAC permissions management - Transparent caching For the next releases, a roadmap is published with many features ideas [2]. Quick start guide to install: http://docs.rackslab.io/slurm-web/install/quickstart.html RPM and deb packages are published for easy installation and upgrade on all most popular Linux distributions. I hope you will like it! [1] https://github.com/rackslab/Slurm-web [2] https://slurm-web.com/roadmap/ -- Rémi Palancher Rackslab: Open Source Solutions for HPC Operations https://rackslab.io

1 0

srun launched mpi job occasionally core dumps
by Henderson, Brent 10 May '24

10 May '24

Greetings Slurm gurus -- I've been having an issue where very occasionally an srun launched OpenMPI job launched will die during startup within MPI_Init(). E.g. srun -N 8 --ntasks-per-node=1 ./hello_world_mpi. Same binary launched with mpirun does not experience the issue. E.g. mpirun -n 64 -H cn01,... ./hello_world_mpi. The failure rate seems to be in the 0.5% - 1.0% range when using srun for launch. SW stack is self-built with: * Dual socket AMD nodes * RHEL 9.3 base system + tools * Single 100 Gb card per host * hwloc 2.9.3 * pmix 4.2.9 (5.0.2 also tried but continued to see the same issues) * slurm 23.11.6 (started with 23.11.5 - update did not change the behavior) * openmpi 5.0.3 The MPI code is a simple hello_world_mpi.c - anything that goes through startup via srun - does not seem to matter. Application core dump looks like the following regardless of the test running: [cn04:1194785] *** Process received signal *** [cn04:1194785] Signal: Segmentation fault (11) [cn04:1194785] Signal code: Address not mapped (1) [cn04:1194785] Failing at address: 0xe0 [cn04:1194785] [ 0] /lib64/libc.so.6(+0x54db0)[0x7f54e6254db0] [cn04:1194785] [ 1] /share/openmpi/5.0.3/lib/libmpi.so.40(mca_pml_ob1_recv_frag_callback_match+0x7d)[0x7f54e67eab3d] [cn04:1194785] [ 2] /share/openmpi/5.0.3/lib/libopen-pal.so.80(+0xa7d8c)[0x7f54e6566d8c] [cn04:1194785] [ 3] /lib64/libevent_core-2.1.so.7(+0x21b88)[0x7f54e649cb88] [cn04:1194785] [ 4] /lib64/libevent_core-2.1.so.7(event_base_loop+0x577)[0x7f54e649e7a7] [cn04:1194785] [ 5] /share/openmpi/5.0.3/lib/libopen-pal.so.80(+0x222af)[0x7f54e64e12af] [cn04:1194785] [ 6] /share/openmpi/5.0.3/lib/libopen-pal.so.80(opal_progress+0x85)[0x7f54e64e1365] [cn04:1194785] [ 7] /share/openmpi/5.0.3/lib/libmpi.so.40(ompi_mpi_init+0x46d)[0x7f54e663ce7d] [cn04:1194785] [ 8] /share/openmpi/5.0.3/lib/libmpi.so.40(MPI_Init+0x5e)[0x7f54e66711ae] [cn04:1194785] [ 9] /home/brent/bin/ior-3.0.1/ior[0x403780] [cn04:1194785] [10] /lib64/libc.so.6(+0x3feb0)[0x7f54e623feb0] [cn04:1194785] [11] /lib64/libc.so.6(__libc_start_main+0x80)[0x7f54e623ff60] [cn04:1194785] [12] /home/brent/bin/ior-3.0.1/ior[0x4069d5] [cn04:1194785] *** End of error message *** More than one rank can die with the same stacktrace on a node when this happens - I've seen as many as 6. One other interesting note is that if I change my srun command line to include strace (e.g. srun -N 8 --ntasks-per-node=8 strace <strace-options> ./hello_world_mpi) the issue appears to go away. 0 failures in ~2500 runs. Another thing that seems to help is to disabling cgroups in the slurm.conf. After the change, saw 0 failures in >6100 hello_world_mpi runs. The changes in the slurm.conf were - original: ProctrackType=proctrack/cgroup TaskPlugin=task/cgroup,task/affinity JobAcctGatherType=jobacct_gather/cgroup Changed ProctrackType=proctrack/linuxproc TaskPlugin=task/affinity JobAcctGatherType=jobacct_gather/linux My cgroup.conf file contains: ConstrainCores=yes ConstrainDevices=yes ConstrainRAMSpace=yes ConstrainSwapSpace=yes AllowedRamSpace=95 Curious is anyone has any thoughts on next steps to help figure out what might be going on and how to resolve it. Currently, I'm planning to back down to the 23.02.7 release and see how that goes but open to other suggestions. Thanks, Brent

2 4

Issues with MIG after update to 23.11
by Cumer Cristiano 10 May '24

10 May '24

Dear list, after an update form 22.05 to 23.11 a host where I'm using MIG started to discard the mig devices during the NVML setuop. I can see those lines in the log: slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) where the MIG devices are discarded during the start of the slurmd deamon. this is my gres.conf file cat /etc/slurm/gres.conf ################################################################## # Slurm's Generic Resource (GRES) configuration file ################################################################## AutoDetect=nvml and that still works for hosts on 22.05 Any idea of what is going on? I tried to disable MIG on the upgraded server, and then all the A100 are recognized without issues. Thanks Cristiano slurmd: debug: Log file re-opened slurmd: debug2: hwloc_topology_init slurmd: debug2: hwloc_topology_load slurmd: debug2: hwloc_topology_export_xml slurmd: debug: CPUs:64 Boards:1 Sockets:2 CoresPerSocket:32 ThreadsPerCore:1 slurmd: debug: cgroup/v1: init: Cgroup v1 plugin loaded slurmd: debug2: hwloc_topology_init slurmd: debug2: xcpuinfo_hwloc_topo_load: xml file (/var/lib/slurm/slurmd/hwloc_topo_whole.xml) found slurmd: debug: CPUs:64 Boards:1 Sockets:2 CoresPerSocket:32 ThreadsPerCore:1 slurmd: debug: gres/gpu: init: loaded slurmd: debug: gpu/nvml: init: init: GPU NVML plugin loaded slurmd: debug2: gpu/nvml: _nvml_init: Successfully initialized NVML slurmd: debug: gpu/nvml: _get_system_gpu_list_nvml: Systems Graphics Driver Version: 550.54.15 slurmd: debug: gpu/nvml: _get_system_gpu_list_nvml: NVML Library Version: 12.550.54.15 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVML API Version: 11 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Total CPU count: 64 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device count: 4 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 0: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-808805ee-2ae5-ee6d-14fd-6e63028d4a2a slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:1:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:01:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: -1,0,0,0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 24-31 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 24-31 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: enabled slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG count: 2 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 0, MIG index 0: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-ab1b2cb1-d690-5564-885e-056f23c1c618 slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-ab1b2cb1-d690-5564-885e-056f23c1c618 slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 1 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 12 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 13 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia0,/dev/nvidia-caps/nvidia-cap12,/dev/nvidia-caps/nvidia-cap13 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 0, MIG index 1: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-c43ecbcb-9f3f-5972-8eb9-6a2a09848bd5 slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-c43ecbcb-9f3f-5972-8eb9-6a2a09848bd5 slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 2 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 21 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 22 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia0,/dev/nvidia-caps/nvidia-cap21,/dev/nvidia-caps/nvidia-cap22 slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 1: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-05911075-e3e1-973b-708a-33a77ddd381c slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:65:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:41:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: 0,-1,0,0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia1 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 8-15 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 8-15 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: enabled slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG count: 2 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 1, MIG index 0: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-36b02f58-efa6-5071-9956-510c8a8e705a slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-36b02f58-efa6-5071-9956-510c8a8e705a slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 1 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 147 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 148 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia1,/dev/nvidia-caps/nvidia-cap147,/dev/nvidia-caps/nvidia-cap148 slurmd: debug2: gpu/nvml: _handle_mig: GPU minor 1, MIG index 1: slurmd: debug2: gpu/nvml: _handle_mig: MIG Profile: nvidia_a100_3g.40gb slurmd: debug2: gpu/nvml: _handle_mig: MIG UUID: MIG-c5ca2779-f34d-544b-af52-55ef14aa0af6 slurmd: debug2: gpu/nvml: _handle_mig: UniqueID: MIG-c5ca2779-f34d-544b-af52-55ef14aa0af6 slurmd: debug2: gpu/nvml: _handle_mig: GPU Instance (GI) ID: 2 slurmd: debug2: gpu/nvml: _handle_mig: Compute Instance (CI) ID: 0 slurmd: debug2: gpu/nvml: _handle_mig: GI Minor Number: 156 slurmd: debug2: gpu/nvml: _handle_mig: CI Minor Number: 157 slurmd: debug2: gpu/nvml: _handle_mig: Device Files: /dev/nvidia1,/dev/nvidia-caps/nvidia-cap156,/dev/nvidia-caps/nvidia-cap157 slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 2: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-7b67964f-9217-908f-042f-f37f06cbb1a2 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:129:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:81:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: 0,0,-1,4 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia2 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 56-63 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 56-63 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: disabled slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: GPU index 3: slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Name: nvidia_a100-sxm4-80gb slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: UUID: GPU-87d7526f-6a29-214e-1c24-722a20211263 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Domain/Bus/Device: 0:193:0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: PCI Bus ID: 00000000:C1:00.0 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: NVLinks: 0,0,4,-1 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Device File (minor number): /dev/nvidia3 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: CPU Affinity Range - Machine: 40-47 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: Core Affinity Range - Abstract: 40-47 slurmd: debug2: gpu/nvml: _get_system_gpu_list_nvml: MIG mode: disabled slurmd: debug2: Possible GPU Memory Frequencies (1): slurmd: debug2: ------------------------------- slurmd: debug2: *1593 MHz [0] slurmd: debug2: Possible GPU Graphics Frequencies (81): slurmd: debug2: --------------------------------- slurmd: debug2: *1410 MHz [0] slurmd: debug2: *1395 MHz [1] slurmd: debug2: ... slurmd: debug2: *810 MHz [40] slurmd: debug2: ... slurmd: debug2: *225 MHz [79] slurmd: debug2: *210 MHz [80] slurmd: gpu/nvml: _get_system_gpu_list_nvml: 4 GPU system device(s) detected slurmd: debug: Gres GPU plugin: Merging configured GRES with system GPUs slurmd: debug2: gres/gpu: _merge_system_gres_conf: gres_list_conf: slurmd: debug2: GRES[gpu] Type:a100_3g.39gb Count:4 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: debug2: GRES[gpu] Type:a100-sxm4-80gb Count:2 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: debug: gres/gpu: _merge_system_gres_conf: Including the following GPU matched between system and configuration: slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):56-63 Links:0,0,-1,4 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia2 UniqueId:(null) slurmd: debug: gres/gpu: _merge_system_gres_conf: Including the following GPU matched between system and configuration: slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):40-47 Links:0,0,4,-1 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia3 UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: error: Discarding the following config-only GPU due to lack of File specification: slurmd: error: GRES[gpu] Type:a100_3g.39gb Count:1 Cores(64):(null) Links:(null) Flags:HAS_TYPE,ENV_NVML,ENV_RSMI,ENV_ONEAPI,ENV_OPENCL,ENV_DEFAULT File:(null) UniqueId:(null) slurmd: warning: The following autodetected GPUs are being ignored: slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):24-31 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia0,/dev/nvidia-caps/nvidia-cap12,/dev/nvidia-caps/nvidia-cap13 UniqueId:MIG-ab1b2cb1-d690-5564-885e-056f23c1c618 slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):24-31 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia0,/dev/nvidia-caps/nvidia-cap21,/dev/nvidia-caps/nvidia-cap22 UniqueId:MIG-c43ecbcb-9f3f-5972-8eb9-6a2a09848bd5 slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):8-15 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia1,/dev/nvidia-caps/nvidia-cap147,/dev/nvidia-caps/nvidia-cap148 UniqueId:MIG-36b02f58-efa6-5071-9956-510c8a8e705a slurmd: GRES[gpu] Type:nvidia_a100_3g.40gb Count:1 Cores(64):8-15 Links:(null) Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia1,/dev/nvidia-caps/nvidia-cap156,/dev/nvidia-caps/nvidia-cap157 UniqueId:MIG-c5ca2779-f34d-544b-af52-55ef14aa0af6 slurmd: debug2: gres/gpu: _merge_system_gres_conf: gres_list_gpu slurmd: debug2: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):56-63 Links:0,0,-1,4 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia2 UniqueId:(null) slurmd: debug2: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):40-47 Links:0,0,4,-1 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia3 UniqueId:(null) slurmd: debug: Gres GPU plugin: Final merged GRES list: slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):56-63 Links:0,0,-1,4 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia2 UniqueId:(null) slurmd: debug: GRES[gpu] Type:a100-sxm4-80gb Count:1 Cores(64):40-47 Links:0,0,4,-1 Flags:HAS_FILE,HAS_TYPE,ENV_NVML File:/dev/nvidia3 UniqueId:(null) slurmd: Gres Name=gpu Type=a100-sxm4-80gb Count=1 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: Gres Name=gpu Type=a100-sxm4-80gb Count=1 Flags=HAS_FILE,HAS_TYPE,ENV_NVML slurmd: topology/default: init: topology Default plugin loaded slurmd: CPU frequency setting not configured for this node slurmd: debug: Resource spec: No specialized cores configured by default on this node slurmd: debug: Resource spec: Reserved system memory limit not configured for this node slurmd: debug: task/cgroup: init: Tasks containment cgroup plugin loaded slurmd: task/affinity: init: task affinity plugin loaded with CPU mask 0xffffffffffffffff slurmd: debug: spank: opening plugin stack /var/lib/slurm/slurmd/conf-cache/plugstack.conf slurmd: debug: /var/lib/slurm/slurmd/conf-cache/plugstack.conf: 1: include "/etc/slurm/plugstack.conf.d/*.conf" slurmd: cred/munge: init: Munge credential signature plugin loaded slurmd: warning: Core limit is only 0 KB slurmd: slurmd version 23.11.4 started slurmd: debug2: No acct_gather.conf file (/var/lib/slurm/slurmd/conf-cache/acct_gather.conf) slurmd: debug: jobacct_gather/cgroup: init: Job accounting gather cgroup plugin loaded slurmd: debug: MPI: Loading all types slurmd: debug: mpi/pmix_v4: init: PMIx plugin loaded slurmd: debug: mpi/pmix_v4: init: PMIx plugin loaded slurmd: debug2: No mpi.conf file (/var/lib/slurm/slurmd/conf-cache/mpi.conf) slurmd: slurmd started on Fri, 10 May 2024 14:30:30 +0000 slurmd: CPUs=64 Boards=1 Sockets=2 Cores=32 Threads=1 Memory=1031664 TmpDisk=0 Uptime=2512944 CPUSpecList=(null) FeaturesAvail=(null) FeaturesActive=(null) ^Cslurmd: got shutdown request slurmd: all threads complete slurmd: debug: mpi/pmix_v4: fini: (null) [0]: mpi_pmix.c:203: (null): call fini() slurmd: debug: mpi/pmix_v4: fini: (null) [0]: mpi_pmix.c:203: (null): call fini() slurmd: debug: jobacct_gather/cgroup: fini: Job accounting gather cgroup plugin unloaded slurmd: debug2: acct_gather_profile_startpoll: poll already ended! slurmd: debug: task/cgroup: fini: Tasks containment cgroup plugin unloaded slurmd: debug: task/affinity: fini: task affinity plugin unloaded slurmd: debug: hash/k12: fini: fini: unloading KangarooTwelve hash plugin slurmd: debug: gres/gpu: fini: unloading ^Cslurmd: debug2: gpu/nvml: _nvml_shutdown: Successfully shut down NVML slurmd: debug: gpu/nvml: fini: fini: unloading GPU NVML plugin slurmd: debug2: acct_gather_profile_startpoll: poll already ended! slurmd: debug: cgroup/v1: fini: unloading Cgroup v1 plugin slurmd: cred/munge: fini: Munge credential signature plugin unloaded slurmd: Slurmd shutdown completing

1 0

Trouble Running Slurm C Extension Plugin
by Glen MacLachlan 10 May '24

10 May '24

Hi, We have a plugin in Lua that mostly does what we want but there are features available in the C extension that are not available to lua. For that reason, we are attempting to convert to C using the guidance found here: https://slurm.schedmd.com/job_submit_plugins.html#building. We arrived here because the lua plugins don't seem to stretch enough to cover the use case we were looking at, i.e., branching off of the value of alloc_id or, for that matter, get_sid(). The goal is to disallow interactive allocations (i.e., salloc) on specific partitions while allowing it on others. However, we've run into an issue with our C plugin right out of the gate and I've included a minimal reproducer as an example which is basically a "Hello World" type of test (job_submit_disallow_salloc.c, see attached). *Expectation* What we expect to happen is a sort of hello-world result with a message being written to a /tmp/min_repo.log but that does not occur. It seems that the plugin does not get run at all when jobs are submitted. Jobs still run as expected but the plugin seems to be ignored. *Steps* We compile gcc -fPIC -DHAVE_CONFIG_H -I /modules/source/slurm-23.02.4 -g -O2 -pthread -fno-gcse -Werror -Wall -g -O0 -fno-strict-aliasing -MT job_submit_disallow_salloc.lo -MD -MP -MF .deps/job_submit_disallow_salloc.Tpo -c job_submit_disallow_salloc.c -o .libs/job_submit_disallow_salloc.o mv .deps/job_submit_disallow_salloc.Tpo .deps/job_submit_disallow_salloc.Plo and link gcc -shared -fPIC -DPIC .libs/job_submit_disallow_salloc.o -O2 -pthread -O0 -pthread -Wl,-soname -Wl,job_submit_disallow_salloc.so -o job_submit_disallow_salloc.so Check links after copying to /usr/lib64/slurm: ldd /usr/lib64/slurm/job_submit_disallow_salloc.so linux-vdso.so.1 (0x00007ffe467aa000) libpthread.so.0 => /lib64/libpthread.so.0 (0x00007f1c02095000) libc.so.6 => /lib64/libc.so.6 (0x00007f1c01cd0000) /lib64/ld-linux-x86-64.so.2 (0x00007f1c024b7000) Can someone point out what we are doing incorrectly or how we might troubleshoot this issue? Kindest regards, Glen *Reproducer* The minimal reproducer is basically a "hello world" for C extensions which I've pasted below (I've also attached it for convenience): #include <slurm/slurm.h> #include <slurm/slurm_errno.h> #include <stdio.h> #include "src/slurmctld/slurmctld.h" const char plugin_name[] = "Min Reproducer"; const char plugin_type[] = "job_submit/disallow_salloc"; const uint32_t plugin_version = SLURM_VERSION_NUMBER; extern int job_submit(job_desc_msg_t *job_desc, uint32_t submit_uid, char **err_msg) { FILE *fp; fp = fopen("/tmp/min_repo.log", "w"); fprintf(fp,"Hello!"); fclose(fp); return SLURM_SUCCESS; } int job_modify(job_desc_msg_t *job_desc, job_record_t *job_ptr, uint32_t submit_uid, char **err_msg) { return SLURM_SUCCESS; }

3 2

RunTimeQuery never configured in oci.conf
by Arnuld 10 May '24

10 May '24

I am using Slurm integrated with Podman. It runs the container fine and the Controller daemon log always says "WEXITSTATUS 0" . Container also runs successfully (it runs the python test program with no errors). But there are two things that I noticed: - slurmd.log says: " error: _get_container_state: RunTimeQuery failed rc:256 output:RunTimeQuery never configured in oci.conf" - podman --log-level=debug reports: "[conmon:d]: failed to write to /proc/self/oom_score_adj: Permission denied" How can I diagnose further to know what's wrong

1 0

GPU GRES verification and some really broad questions.
by Shooktija S N 10 May '24

10 May '24

Hi, I am a complete slurm-admin and sys-admin noob trying to set up a 3 node Slurm cluster. I have managed to get a minimum working example running, in which I am able to use a GPU (NVIDIA GeForce RTX 4070 ti) as a GRES. This is *slurm.conf* without the comment lines: root@server1:/etc/slurm# grep -v "#" slurm.confClusterName=DlabClusterSlurmctldHost=server1GresTypes=gpuProctrackType=proctrack/linuxprocReturnToService=1SlurmctldPidFile=/var/run/slurmctld.pidSlurmctldPort=6817SlurmdPidFile=/var/run/slurmd.pidSlurmdPort=6818SlurmdSpoolDir=/var/spool/slurmdSlurmUser=rootStateSaveLocation=/var/spool/slurmctldTaskPlugin=task/affinity,task/cgroupInactiveLimit=0KillWait=30MinJobAge=300SlurmctldTimeout=120SlurmdTimeout=300Waittime=0SchedulerType=sched/backfillSelectType=select/cons_tresJobCompType=jobcomp/noneJobAcctGatherFrequency=30SlurmctldDebug=infoSlurmctldLogFile=/var/log/slurmctld.logSlurmdDebug=debug3SlurmdLogFile=/var/log/slurmd.logNodeName=server[1-3] RealMemory=128636 Sockets=1 CoresPerSocket=64 ThreadsPerCore=2 State=UNKNOWN Gres=gpu:1PartitionName=mainPartition Nodes=ALL Default=YES MaxTime=INFINITE State=UP This is *gres.conf* (only one line), each node has been assigned its corresponding NodeName: root@server1:/etc/slurm# cat gres.confNodeName=server1 Name=gpu File=/dev/nvidia0 Those are the only config files I have. I have a few general questions, loosely arranged in ascending order of generality: 1) I have enabled the allocation of GPU resources as a GRES and have tested this by running: shookti@server1:~$ srun --nodes=3 --gpus=3 --label hostname2: server30: server11: server2 Is this a good way to check if the configs have worked correctly? How else can I easily check if the GPU GRES has been properly configured? 2) I want to reserve a few CPU cores, and a few gigs of memory for use by non slurm related tasks. According to the documentation, I am to use CoreSpecCount <https://slurm.schedmd.com/slurm.conf.html#OPT_CoreSpecCount> and MemSpecLimit <https://slurm.schedmd.com/slurm.conf.html#OPT_MemSpecLimit> to achieve this. The documentation for CoreSpecCount says "the Slurm daemon slurmd may either be confined to these resources (the default) or prevented from using these resources", how do I change this default behaviour to have the config specify the cores reserved for non slurm stuff instead of specifying how many cores slurm can use? 3) While looking up examples online on how to run Python scripts inside a conda env, I have seen that the line 'module load conda' should be run before running 'conda activate myEnv' in the sbatch submission script. The command 'module' did not exist until I installed the apt package 'environment-modules', but now I see that conda is not listed as a module that can be loaded when I check using the command 'module avail'. How do I fix this? 4) A very broad question: while managing the resources being used by a program, slurm might happen to split the resources across multiple computers that might not necessarily have the files required by this program to run. For example, a python script that requires the package 'numpy' to function but that package was not installed on all of the computers. How are such things dealt with? Is the module approach meant to fix this problem? In my previous question, if I had a python script that users usually run just by running a command like 'python3 someScript.py' instead of running it within a conda environment, how should I enable slurm to manage the resources required by this script? Would I have to install all the packages required by this script on all the computers that are in the cluster? 5) Related to the previous question: I have set up my 3 nodes in such a way that all the users' home directories are stored on a ceph cluster <https://en.wikipedia.org/wiki/Ceph_(software)> created using the hard drives from all the 3 nodes, which essentially means that a user's home directory is mounted at the same location on all 3 computers - making a user's data visible to all 3 nodes. Does this make the process of managing the dependencies of a program as described in the previous question easier? I realise that programs having to read and write to files on the hard drives of a ceph cluster is not really the fastest so I am planning on having users use the /tmp/ directory for speed critical reading and writing, as the OSs have been installed on NVME drives.

2 1

Problems with gres.conf
by Gestió Servidors 10 May '24

10 May '24

Hello, I am trying to rewrite my gres.conf file. Before changes, this file was just like this: NodeName=node-gpu-1 AutoDetect=off Name=gpu Type=GeForceRTX2070 File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-1 AutoDetect=off Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia1 Cores=12-23 NodeName=node-gpu-2 AutoDetect=off Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-2 AutoDetect=off Name=gpu Type=GeForceGTX1080 File=/dev/nvidia1 Cores=12-23 NodeName=node-gpu-3 AutoDetect=off Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Cores=0-11 NodeName=node-gpu-4 AutoDetect=off Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Cores=0-7 # you can seee that nodes node-gpu-1 and node-gpu-2 have two GPUs each one, whereas nodes node-gpu-3 and node-gpu-4 have only one GPU each one And my slurmd.conf was this: [...] AccountingStorageTRES=gres/gpu GresTypes=gpu NodeName=node-gpu-1 CPUs=24 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceRTX2070:1,gpu:GeForceGTX1080Ti:1 NodeName=node-gpu-2 CPUs=24 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceGTX1080Ti:1,gpu:GeForceGTX1080:1 NodeName=node-gpu-3 CPUs=12 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=23000 Gres=gpu:GeForceRTX3080:1 NodeName=node-gpu-4 CPUs=8 SocketsPerBoard=1 CoresPerSocket=4 ThreadsPerCore=2 RealMemory=7800 Gres=gpu:GeForceRTX3080:1 NodeName=node-worker-[0-22] CPUs=12 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=47000 [...] With this configuration, all seems works fine, except slurmctld.log reports: [...] error: _node_config_validate: gres/gpu: invalid GRES core specification (0-11) on node node-gpu-3 error: _node_config_validate: gres/gpu: invalid GRES core specification (12-23) on node node-gpu-1 error: _node_config_validate: gres/gpu: invalid GRES core specification (12-23) on node node-gpu-2 error: _node_config_validate: gres/gpu: invalid GRES core specification (0-7) on node node-gpu-4 [...] However, even these errors, users can submit jobs and request GPUs resources. Now, I have tried to reconfigure gres.conf and slurmd.conf in this way: gres.conf: Name=gpu Type=GeForceRTX2070 File=/dev/nvidia0 Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia1 Name=gpu Type=GeForceGTX1080Ti File=/dev/nvidia0 Name=gpu Type=GeForceGTX1080 File=/dev/nvidia1 Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 Name=gpu Type=GeForceRTX3080 File=/dev/nvidia0 # there is no NodeName attribute slurmd.conf: [...] NodeName=node-gpu-1 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceRTX2070:1,gpu:GeForceGTX1080Ti:1 NodeName=node-gpu-2 SocketsPerBoard=2 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=96000 TmpDisk=47000 Gres=gpu:GeForceGTX1080Ti:1,gpu:GeForceGTX1080:1 NodeName=node-gpu-3 SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=23000 Gres=gpu:GeForceRTX3080:1 NodeName=node-gpu-4 SocketsPerBoard=1 CoresPerSocket=4 ThreadsPerCore=2 RealMemory=7800 Gres=gpu:GeForceRTX3080:1 NodeName=node-worker-[0-22] SocketsPerBoard=1 CoresPerSocket=6 ThreadsPerCore=2 RealMemory=47000 # there is no CPUs attribute [...] With this new configuration, nodes with GPU start correctly slurmd.service daemon, but nodes without GPU (node-worker-[0-22]) can't start slurmd.service daemon and returns this error: [...] error: Waiting for gres.conf file /dev/nvidia0 fatal: can't stat gres.conf file /dev/nvidia0: No such file or directory [...] It seems SLURM is waiting that "node-workers" have also an nvidia GPU but not, theses nodes haven't GPU... So, where is my configuration error? I have read in https://slurm.schedmd.com/gres.conf.html about syntax and examples but it seems I'm doing some wrong. Thanks!!

1 0

Re: srun launched mpi job occasionally core dumps
by Henderson, Brent 08 May '24

08 May '24

Thanks for the suggestion Ole - I'll see if I can get that in the mix to try over the next few days. I can report that 23.02.7 tree had the same issues, so going backwards on the slurm bits did not have any impact. Brent

1 0

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