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.
srun
srun is the command used to run a process on the compute nodes in the cluster. It works by passing it a command (this could be a script) which will be run on a compute node and then srun will 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.
tgray26@opensub01:srun --mem=100mb --time=1:00:00 bash -c 'echo "Hello World from" `hostname`' Hello World from openlab06.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 session that you can submit to the cluster and 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
--mem=1gbif no unit is given MB is assumed--nodes=2if passed to srun, the given command will be run concurrently on each node--qos=dpart--time=hh:mm:sstime needed to run your job--job-name=helloWorld--output filenamefile to redirect stdout to--error filenamefile to redirect stderr--partition $PNAMErequest job run in the $PNAME partition--ntasks 2request 2 "tasks" which map to cores on a CPU, if passed to srun the given command will be run concurrently on each core
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 to invoke a shell:
tgray26@opensub01:srun --pty --mem 1gb --time=01:00:00 bash tgray26@openlab06:
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.
tgray26@opensub00:salloc -N 1 --mem=2gb --time=01:00:00 salloc: Granted job allocation 159 tgray26@opensub00:srun /usr/bin/hostname openlab00.umiacs.umd.edu tgray26@opensub00: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:
tgray26@opensub00:sbatch --mem=1gb --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:
tgray26@opensub00:squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
121 dpart helloWor tgray26 R 0:01 2 openlab[00-01]
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 10s/100s/1000s 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 I have a batch job that will request 2 nodes in the cluster, then I load a specific version of Python into my environment and submit two job steps, each one using one node. Since srun is blocks until the command finishes, I use the '&' operator to background the process so that both job steps can run at once; however, this means that I 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 #SBATCH --job-name=helloWorld # sets the job name #SBATCH --output helloWorld.out.%j # indicates a file to redirect STDOUT to; %j is the jobid #SBATCH --error helloWorld.out.%j # indicates a file to redirect STDERR to; %j is the jobid #SBATCH --time=00:05:00 # how long you think your job will take to complete; format=hh:mm:ss #SBATCH --qos=dpart # set QOS, this will determine what resources can be requested #SBATCH --nodes=2 # number of nodes to allocate for your job #SBATCH --ntasks=4 # request 4 cpu cores be reserved for your node total #SBATCH --ntasks-per-node=2 # request 2 cpu cores be reserved per node #SBATCH --mem 1gb # memory required by job; if unit is not specified MB will be assumed module load Python/2.7.9 # run any commands necessary to setup your environment srun -N 1 --mem=512mb bash -c "hostname; python --version" & # use srun to invoke commands within your job; using an '&' srun -N 1 --mem=512mb bash -c "hostname; python --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
More Examples
More examples of how to use batch scripts to setup your environment for processing will be coming soon
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 255cancel job 255scancel 255.3cancel job step 3 of job 255scancel --user tgray26 --partition dpartcancel all jobs for tgray26 in the dpart partition
Identifying Resources and Features
The sinfo 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 this
sinfo -o "%15N %10c %10m %25f %10G".
$ sinfo -o "%15N %10c %10m %25f %10G" NODELIST CPUS MEMORY AVAIL_FEATURES GRES openlab[00-07] 8 7822 (null) (null) openlab08 16 128720 (null) gpu:k20:2 openlab09 16 128722 (null) gpu:3
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. You will also need to make sure that you submit your job to the correct partition since nodes with GPUs are often put into their own partition to prevent the nodes from being tied up by jobs that don't utilize GPUs. 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:2 or 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 --gres:k20:1
tgray26@opensub01:srun --pty --partition gpu --gres=gpu:2 nvidia-smi Wed Jul 13 15:33:18 2016 +------------------------------------------------------+ | NVIDIA-SMI 361.28 Driver Version: 361.28 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K20c Off | 0000:03:00.0 Off | 0 | | 30% 24C P0 48W / 225W | 11MiB / 4799MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K20c Off | 0000:84:00.0 Off | 0 | | 30% 23C P0 52W / 225W | 11MiB / 4799MiB | 93% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name 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 request only 1 GPU and the other one will be left available for other users:
tgray26@opensub01:srun --pty --partition gpu --gres=gpu:k20:1 nvidia-smi Wed Jul 13 15:31:29 2016 +------------------------------------------------------+ | NVIDIA-SMI 361.28 Driver Version: 361.28 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K20c Off | 0000:03:00.0 Off | 0 | | 30% 24C P0 50W / 225W | 11MiB / 4799MiB | 92% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
The --gres flag may also be passed to sbatch and salloc rather than directly to srun