SLURM/JobSubmission

Job Submission

SLURM offers a variety of ways to run jobs. It is important to understand the different options available and how to request the resources required for a job in order for it to run successfully. All job submission should be done from submit nodes; any computational code should be run in a job allocation on compute nodes. The following commands outline how to allocate resources on the compute nodes and submit processes to be run on the allocated nodes.

The cluster that everyone with a UMIACS account has access to is Nexus. Please visit the Nexus page for instructions on how to connect to your assigned submit nodes.

Computationally intensive processes run on submission nodes will be terminated. Please submit jobs to be scheduled on compute nodes for this purpose.

For details on how SLURM decides how to schedule jobs when multiple jobs are waiting in a scheduler's queue, please see SLURM/Priority.

srun

The srun command is used to run a process on the compute nodes in the cluster. If you pass it a normal shell command (or command that executes a script), it will submit a job to run that shell command/script on a compute node and then return. srun accepts many command line options to specify the resources required by the command passed to it. Some common command line arguments are listed below and full documentation of all available options is available in the man page for srun, which can be accessed by running man srun.

$ srun --qos=default --mem=100mb --time=1:00:00 bash -c 'echo "Hello World from" `hostname`'
Hello World from tron33.umiacs.umd.edu

It is important to understand that srun is an interactive command. By default input to srun is broadcast to all compute nodes running your process and output from the compute nodes is redirected to srun. This behavior can be changed; however, srun will always wait for the command passed to finish before exiting, so if you start a long running process and end your terminal session, your process will stop running on the compute nodes and your job will end. To run a non-interactive submission that will remain running after you logout, you will need to wrap your srun commands in a batch script and submit it with sbatch.

Common srun Arguments

• --job-name=<JOBNAME> Requests your job be named <JOBNAME>
• --mem=1g Requests 1GB of memory for your job, if no unit is given MB is assumed
• --ntasks=2 Requests 2 "tasks" which map to cores on a CPU for your job; if passed to srun, runs the given command concurrently on each core
• --nodes=2 Requests 2 nodes be allocated to your job; if passed to srun, runs the given command concurrently on each node
• --nodelist=<NODENAME> Requests to run your job on the <NODENAME> node
• --time=dd-hh:mm:ss Requests your job run for dd days, hh hours, mm minutes, and ss seconds
• --error=<ERRNAME> Redirects stderr for your job to the <ERRNAME> file
• --partition=<PARTITIONNAME> Requests your job run in the <PARTITIONNAME> partition
• --qos=<QOSNAME>default Requests your job run with the <QOSNAME> QOS, to see the available QOS options on a cluster, run show_qos
• --account=<ACCOUNTNAME> Requests your job runs under the <ACCOUNTNAME> Slurm account, different accounts have different available partitions/QOS
• --output=<OUTNAME> Redirects stdout for your job to the <OUTNAME> file
• --requeue Requests your job be automatically requeued if it is preempted
• --exclusive Requests your job be the only one running on the node(s) it is assigned to. This requires that your job be allocated all of the resources on the node(s). The scheduler does not automatically give your job all of the node's/nodes' resources, however, so if you need more than the default, you still need to request these with --ntasks and --mem

Interactive Shell Sessions

An interactive shell session on a compute node can be useful for debugging or developing code that isn't ready to be run as a batch job. To get an interactive shell on a node, use srun with the --pty argument to invoke a shell:

$ srun --pty --qos=default --mem=1g --time=01:00:00 bash
$ hostname
tron33.umiacs.umd.edu

Please do not leave interactive shells running for long periods of time when you are not working. This blocks resources from being used by everyone else.

salloc

The salloc command can also be used to request resources be allocated without needing a batch script. Running salloc with a list of resources will allocate the resources you requested, create a job, and drop you into a subshell with the environment variables necessary to run commands in the newly created job allocation. When your time is up or you exit the subshell, your job allocation will be relinquished.

$ salloc --qos=default -N 1 --mem=2g --time=01:00:00
salloc: Granted job allocation 159
$ srun /usr/bin/hostname
tron33.umiacs.umd.edu
$ exit
exit
salloc: Relinquishing job allocation 159

Please note that any commands not invoked with srun will be run locally on the submit node. Please be careful when using salloc.

sbatch

The sbatch command allows you to write a batch script to be submitted and run non-interactively on the compute nodes. To run a simple Hello World command on the compute nodes you could write a file, helloWorld.sh with the following contents:

#!/bin/bash

srun bash -c 'echo Hello World from `hostname`'

Then you need to submit the script with sbatch and request resources:

$ sbatch --qos=default --mem=1g --time=1:00:00 helloWorld.sh
Submitted batch job 121

SLURM will return a job number that you can use to check the status of your job with squeue:

$ squeue
             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
               121      tron helloWor username  R       0:01      1 tron32

Advanced Batch Scripts

You can also write a batch script with all of your resources/options defined in the script itself. This is useful for jobs that need to be run tens/hundreds/thousands of times. You can then handle any necessary environment setup and run commands on the resources you requested by invoking commands with srun. The srun commands can also be more complex and be told to only use portions of your entire job allocation - each of these distinct srun commands makes up one "job step". The batch script will be run on the first node allocated as part of your job allocation and each job step will be run on whatever resources you tell them to.

In the following example, we have a batch job that will request 2 nodes in the cluster. We then load a specific version of Python into our environment and submit two job steps, each one using one node. Since srun blocks until the command finishes by default, we use the '&' operator to background the process so that both job steps can run at once; however, this means that we then need to use the wait command to block processing until all background processes have finished.

#!/bin/bash

# Lines that begin with #SBATCH specify commands to be used by SLURM for scheduling. These MUST be above any non-#SBATCH lines in the script to take effect.

#SBATCH --job-name=helloWorld                                        # set job name
#SBATCH --output=helloWorld.out.%j                                   # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail.
#SBATCH --error=helloWorld.err.%j                                    # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail.
#SBATCH --time=00:05:00                                              # how long you would like your job to run; format=dd-hh:mm:ss
#SBATCH --account=nexus                                              # set account, this determines which partitions and QOSes are available for your job
#SBATCH --partition=tron                                             # set partition, this determines what nodes are available for your job
#SBATCH --qos=default                                                # set QOS, this determines how many resources can be requested within your job, and for how long
#SBATCH --nodes=2                                                    # number of nodes to allocate for your job
#SBATCH --ntasks=4                                                   # request 4 cpu cores be reserved for your job total
#SBATCH --ntasks-per-node=2                                          # request 2 cpu cores be reserved per node
#SBATCH --mem=1g                                                     # memory required by job; if unit is not specified MB will be assumed. for multi-node jobs, this argument allocates this much memory *per node*

srun --nodes=1 --mem=512m bash -c "hostname; python3 --version" &    # use srun to invoke commands within your job; using an '&'
srun --nodes=1 --mem=512m bash -c "hostname; python3 --version" &    # will background the process allowing them to run concurrently
wait                                                                 # wait for any background processes to complete

# Once the end of the batch script is reached, your job allocation will be revoked.

Another useful thing to know is that you can pass additional arguments into your sbatch scripts on the command line and reference them as ${1} for the first argument and so on.

More Examples

• SLURM/ArrayJobs

scancel

The scancel command can be used to cancel job allocations or job steps that are no longer needed. It can be passed individual job IDs or an option to delete all of your jobs or jobs that meet certain criteria.

• scancel 255 cancel job 255
• scancel 255.3 cancel job step 3 of job 255
• scancel --user username --partition=tron cancel all jobs for username in the tron partition

Identifying Resources and Features

The sinfo command can show you additional features of nodes in the cluster but you need to ask it to show some non-default options using a command like sinfo -o "%40N %8c %8m %35f %35G".

$ sinfo -o "%40N %10c %10m %40f %32G"
NODELIST                                 CPUS       MEMORY     AVAIL_FEATURES                           GRES
cbcb[00-21]                              32         2061175    rhel8,x86_64,Zen,EPYC-7313               (null)
cbcb22,cmlcpu[00,06-07],legacy20         24+        384270+    rhel8,x86_64,Xeon,E5-2680                (null)
cbcb26                                   128        513243     rhel8,x86_64,Zen,EPYC-7763,Ampere        gpu:rtxa5000:7
cbcb27                                   64         255167     rhel8,x86_64,Zen,EPYC-7513,Ampere        gpu:rtxa6000:8
cbcb[28-29]                              32         771166     rhel8,x86_64,Zen,EPYC-9124,Ada           gpu:rtx6000ada:8
legacy00                                 48         125940     rhel8,x86_64,Zen,EPYC-7402               (null)
cbcb[23-24],legacy[34-35],twist05        24         255150     rhel8,x86_64,Xeon,E5-2650                (null)
cbcb25                                   24         255278     rhel8,x86_64,Xeon,E5-2650,Pascal,Turing  gpu:rtx2080ti:1,gpu:gtx1080ti:1
legacy[01-11,13-19,22-28,30]             12+        61804+     rhel8,x86_64,Xeon,E5-2620                (null)
legacy21                                 8          61746      rhel8,x86_64,Xeon,E5-2623                (null)
legacy31                                 8          61727      rhel8,x86_64,Xeon,E5-1660                (null)
tron[46-61]                              48         255232     rhel8,x86_64,Zen,EPYC-7352,Ampere        gpu:rtxa5000:8
tron[06-09,12-15,21]                     16         126214+    rhel8,x86_64,Zen,EPYC-7302P,Ampere       gpu:rtxa4000:4
tron[10-11,16-20,34]                     16         126217     rhel8,x86_64,Zen,EPYC-7313P,Ampere       gpu:rtxa4000:4
tron[22-33,35-44]                        16         126214+    rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa4000:4
clip13,cml30,vulcan[29-30,32,45]         32         255218+    rhel8,x86_64,Zen,EPYC-7313,Ampere        gpu:rtxa6000:8
cml[17-28],gammagpu05                    32         255225+    rhel8,x86_64,Zen,EPYC-7282,Ampere        gpu:rtxa4000:8
cml12                                    32         383038     rhel8,x86_64,Xeon,4216,Turing,Ampere     gpu:rtx2080ti:7,gpu:rtxa4000:1
cml31                                    32         384094     rhel8,x86_64,Zen,EPYC-9124,Ampere,Hopper gpu:a100:1,gpu:h100-nvl:1
cml32                                    64         512999     rhel8,x86_64,Zen,EPYC-7543,Ampere        gpu:a100:4
cml33                                    64         1029019    rhel8,x86_64,Xeon,6448Y,Hopper           gpu:h100-sxm:4
cmlcpu[01-04]                            20         384271     rhel8,x86_64,Xeon,E5-2660                (null)
cml[00,15-16]                            32         351530+    rhel8,x86_64,Xeon,4216,Turing            gpu:rtx2080ti:7
cml01                                    32         383030     rhel8,x86_64,Xeon,4216,Turing            gpu:rtx2080ti:6
cml[02-11,13],tron[62-63,65-66,68-69]    32         351770+    rhel8,x86_64,Xeon,4216,Turing            gpu:rtx2080ti:8
clip12,gammagpu[10-17]                   16         126203+    rhel8,x86_64,Zen,EPYC-7313,Ampere        gpu:rtxa6000:4
gammagpu[18-21]                          32         254883     rhel8,x86_64,Xeon,6526Y,Ada              gpu:l40s:4
gammagpu00                               32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa5000:8
legacygpu06                              20         255249     rhel8,x86_64,Xeon,E5-2699,Maxwell        gpu:gtxtitanx:4
legacygpu[01-02,07]                      20         255249+    rhel8,x86_64,Xeon,E5-2650,Maxwell        gpu:gtxtitanx:4
legacygpu05                              44         513193     rhel8,x86_64,Xeon,E5-2699,Pascal         gpu:gtx1080ti:4
legacygpu00                              20         255249     rhel8,x86_64,Xeon,E5-2650,Pascal         gpu:titanxp:4
legacygpu[03-04]                         16         255268     rhel8,x86_64,Xeon,E5-2630,Maxwell        gpu:gtxtitanx:2
mbrc[00-01]                              20         189498     rhel8,x86_64,Xeon,4114,Turing            gpu:rtx2080ti:8
clip03                                   20         126243     rhel8,x86_64,Xeon,E5-2630,Pascal,Turing  gpu:rtx2080ti:1,gpu:gtx1080ti:2
clip04                                   32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtx3090:4
clip[05-06]                              24         126216     rhel8,x86_64,Zen,EPYC-7352,Ampere        gpu:rtxa6000:2
clip09                                   32         383043     rhel8,x86_64,Xeon,6130,Pascal,Turing     gpu:rtx2080ti:5,gpu:gtx1080ti:3
clip10                                   44         1029404    rhel8,x86_64,Xeon,E5-2699                (null)
clip11                                   16         126217     rhel8,x86_64,Zen,EPYC-7313,Ampere        gpu:rtxa4000:4
quics00                                  128        1545009    rhel8,x86_64,Zen,EPYC-9534               (null)
tron00                                   32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa6000:6
tron[01-03,05]                           32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa6000:8
tron04                                   32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa6000:7
tron[64,67]                              32         383028+    rhel8,x86_64,Xeon,4216,Turing,Ampere     gpu:rtx2080ti:7,gpu:rtx3070:1
clip00                                   32         255276     rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:titanxpascal:3
clip01                                   32         255276     rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:titanxpascal:1,gpu:titanxp:2
clip02                                   20         126255     rhel8,x86_64,Xeon,E5-2630,Pascal         gpu:gtx1080ti:3
clip07                                   8          255263     rhel8,x86_64,Xeon,E5-2623,Pascal         gpu:gtx1080ti:3
oasis[00-39]                             160        28114      rhel9,aarch64,Altra,Altra-80             (null)
vulcan24                                 16         126216     rhel8,x86_64,Zen,EPYC-7282,Ampere        gpu:rtxa6000:4
gammagpu[01-04,06-07,09],vulcan[33-37]   32         255215+    rhel8,x86_64,Zen,EPYC-7313,Ampere        gpu:rtxa5000:8
vulcan[38-44]                            32         255215     rhel8,x86_64,Zen,EPYC-7313,Ampere        gpu:rtxa4000:8
brigid[16-17]                            48         512897     rhel8,x86_64,Zen,EPYC-7443               (null)
vulcan23                                 32         383030     rhel8,x86_64,Xeon,4612,Turing            gpu:rtx2080ti:8
vulcan[27-28]                            56         770093     rhel8,x86_64,Xeon,8280,Turing            gpu:rtx2080ti:10
brigid[18-19]                            20         61739      rhel8,x86_64,Xeon,E5-2640                (null)
vulcan00                                 32         255259     rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:p6000:7,gpu:p100:1
vulcan[01-02,04,06-07]                   32         255259     rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:p6000:8
vulcan[03,05]                            32         255259     rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:p6000:7
clip08,vulcan[08-16,18,20-22,25]         32         255258+    rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:gtx1080ti:8
vulcan[17,19]                            32         255259     rhel8,x86_64,Xeon,E5-2683,Pascal         gpu:gtx1080ti:7
vulcan26                                 24         770126     rhel8,x86_64,Xeon,6146,Pascal            gpu:titanxp:10

Note that all of the nodes shown by this may not necessarily be in a partition you are able to submit to.

You can identify further specific information about a node using scontrol with various flags.

There are also two command aliases developed by UMIACS staff to show various node information in aggregate. They are show_nodes and show_available_nodes.

show_nodes

The show_nodes command alias shows each node's name, number of CPUs, memory, {OS, CPU architecture, CPU type, GPU architecture (if the node has GPUs)} (as AVAIL_FEATURES), GRES (GPUs), and State. It essentially wraps the sinfo command with some pre-determined output format options and shows each node on its own line, in alphabetical order.

To only view nodes in a specific partition, append -p <partition name> to the command alias.

Examples

$ show_nodes
NODELIST             CPUS       MEMORY     AVAIL_FEATURES                           GRES                             STATE
brigid16             48         512897     rhel8,x86_64,Zen,EPYC-7443               (null)                           idle
brigid17             48         512897     rhel8,x86_64,Zen,EPYC-7443               (null)                           idle
...                  ...        ...        ...                                      ...                              ...
vulcan45             32         513250     rhel8,x86_64,Zen,EPYC-7313,Ampere        gpu:rtxa6000:8                   idle

(specific partition)

$ show_nodes -p tron
NODELIST             CPUS       MEMORY     AVAIL_FEATURES                           GRES                             STATE
tron00               32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa6000:8                   idle
tron01               32         255233     rhel8,x86_64,Zen,EPYC-7302,Ampere        gpu:rtxa6000:8                   idle
...                  ...        ...        ...                                      ...                              ...
tron69               32         383030     rhel8,x86_64,Xeon,4216,Turing            gpu:rtx2080ti:8                  idle

show_available_nodes

The show_available_nodes command alias takes zero or more arguments that correspond to Slurm constructs, resources, or features that you are looking to request a job with and tells you what nodes could theoretically[0,1] run a job with these arguments immediately. It assumes your job is a single-node job.

These arguments are:

• --partition: Only include nodes in the specified partition(s).
• --account: Only include nodes from partitions that can use the specified account(s).
• --qos: Only include nodes from partitions that can use the specified QoS(es).
• --cpus: Only include nodes with at least this many CPUs free.
• --mem: Only include nodes with at least this much memory free. The default unit is MB if unspecified, but any of {K,M,G,T} can be suffixed to the number provided (will then be interpreted as KB, MB, GB, or TB, respectively).
• GRES-related arguments:
  • --gres, --and-gres: Only include nodes whose list of GRES contains all of the specified GRES type/quantity pairings.
  • --or-gres: Only include nodes whose list of GRES contains any of the specified GRES type/quantity pairings. Functionally identical to --and-gres if only one GRES type/quantity pairing is specified.
• GPU-related arguments:
  • --gpus, --and-gpus: Only include nodes whose list of GPUs (a subset of GRES) contains all of the specified GPU type/quantity pairings.
  • --or-gpus: Only include nodes whose list of GPUs (a subset of GRES) contains any of the specified GPU type/quantity pairings. Functionally identical to --and-gpus if only one GPU type/quantity pairing is specified.
• Feature-related arguments:
  • --feature, --and-feature: Only include nodes whose list of features contains all of the specified feature(s).
  • --or-feature: Only include nodes whose list of features contains any of the specified feature(s). Functionally identical to --and-feature if only one feature is specified.

These arguments are also viewable by running show_available_nodes -h.

If your passed argument set does not contain any resource-based arguments (CPUs/RAM/GRES or GPUs), a node is defined as available if it has at least 1 CPU and 1MB of RAM available.

If there are no nodes available that meet your passed argument set, you will receive the message There are no nodes that have currently free resources that meet this argument set.

Footnotes

[0] - As of now, this command alias does not factor in resources occupied by jobs that could be preempted (based on the partition(s) passed to it, if present). This is soon to come.

[1] - This command alias also does not factor in jobs with higher priority values requesting more resources, in the same partition(s), blocking execution of a job submitted with the resources / other arguments checked by the command alias. This is due to the infeasibility of calculating a job's priority value before it is actually submitted.

Examples

Show all available nodes:

$ show_available_nodes
brigid17
  cpus=16,mem=414593M
brigid18
  cpus=8,mem=24875M
...

Show nodes available in the tron partition:

$ show_available_nodes --partition tron
tron00
  cpus=14,mem=50433M,gres=gpu:rtxa6000:1
tron01
  cpus=10,mem=17665M,gres=gpu:rtxa6000:2
...

Show nodes with one or more RTX A5000 or RTX A6000 GPUs available to the vulcan account:

$ show_available_nodes --account vulcan --or-gpus rtxa5000:1,rtxa6000:1
vulcan32
  cpus=16,mem=193778M,gres=gpu:rtxa6000:4
vulcan33
  cpus=15,mem=181499M,gres=gpu:rtxa5000:3
...

Show nodes with 4 or more CPUs, 48G or more memory, and one or more RTX A6000 GPUs available in the scavenger partition:

$ show_available_nodes --partition=scavenger --cpus=4 --mem=48g --or-gpus=rtxa6000:1
cbcb27
  cpus=59,mem=218303M,gres=gpu:rtxa6000:6
clip06
  cpus=20,mem=93448M,gres=gpu:rtxa6000:1
...

Show nodes with Turing or Ampere architecture GPUs available in the scavenger partition:

$ show_available_nodes --partition=scavenger --or-feature=Ampere,Turing
cbcb25
  cpus=24,mem=255278M,gres=gpu:rtx2080ti:1,gpu:gtx1080ti:1
cbcb26
  cpus=127,mem=447707M,gres=gpu:rtxa5000:7
...

Show nodes with Zen architecture CPUs and Ampere architecture GPUs available in the scavenger partition:

$ show_available_nodes --partition=scavenger --and-feature=Zen,Ampere
cbcb26
  cpus=127,mem=447707M,gres=gpu:rtxa5000:7
cbcb27
  cpus=59,mem=218303M,gres=gpu:rtxa6000:6
...

(bogus example) Attempt to show nodes available in the bogus partition:

$ show_available_nodes --partition=bogus
There are no nodes that have currently free resources that meet this argument set.

Requesting GPUs

If you need to do processing on a GPU, you will need to request that your job have access to GPUs just as you need to request processors or CPU cores. In SLURM, GPUs are considered "generic resources" also known as GRES. To request some number of GPUs be reserved/available for your job, you can use the flag --gres=gpu:# (with the actual number of GPUs you want). If there are multiple types of GPUs available in the cluster and you need a specific type, you can provide the type option to the gres flag e.g. --gres=gpu:rtxa5000:#. If you do not request a specific type of GPU, you are likely to be scheduled on an older, lower spec'd GPU.

Note that some QoSes may have limits on the number of GPUs you can request per job, so you may need to specify a different QoS to request more GPUs.

$ srun --pty --qos=medium --gres=gpu:2 nvidia-smi
...
Wed Mar  6 16:59:39 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 2080 Ti     Off | 00000000:3D:00.0 Off |                  N/A |
| 32%   23C    P8               1W / 250W |      0MiB / 11264MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA GeForce RTX 2080 Ti     Off | 00000000:40:00.0 Off |                  N/A |
| 32%   25C    P8               1W / 250W |      0MiB / 11264MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

Please note that your job will only be able to see/access the GPUs you requested. If you only need 1 GPU, please only request 1 GPU. The others on the node (if any) will be left available for other users.

$ srun --pty --gres=gpu:rtxa5000:1 nvidia-smi
Thu Aug 25 15:22:15 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.129.06   Driver Version: 470.129.06   CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA RTX A5000    Off  | 00000000:01:00.0 Off |                  Off |
| 30%   23C    P8    20W / 230W |      0MiB / 24256MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

As with all other flags, the --gres flag may also be passed to sbatch and salloc rather than directly to srun.

MPI example

To run MPI jobs, you will need to include the --mpi=pmix flag in your submission arguments.

#!/usr/bin/bash 
#SBATCH --job-name=mpi_test # Job name 
#SBATCH --nodes=4 # Number of nodes 
#SBATCH --ntasks=8 # Number of MPI ranks 
#SBATCH --ntasks-per-node=2 # Number of MPI ranks per node 
#SBATCH --ntasks-per-socket=1 # Number of tasks per processor socket on the node 
#SBATCH --time=00:30:00 # Time limit hrs:min:sec 


srun --mpi=pmix /nfshomes/username/testing/mpi/a.out