We have a cluster with 1 node, 200 CPU cores and 2T RAM. The server are shared by 15+ people and required to submitted job by slurm.(computing node and login node are on same machine). But some people are unwilling to do so!
So, is there a way to limit the resources of user's process submitted by cmd, but no by slurm?
For example, a no-slurm job shouled be restricted with CPU:2, RAM:4G;
$ resource-consuming-program # job submitted by cmd should be restricted.
$ cat slurmjob.sh
#!/bin/sh
#SBATCH -J TEST
#SBATCH --cpus-per-task=1
#SBATCH --mem=700G
# We recommend using SLURM to run resource-consuming job.
resource-consuming-program
$ sbatch slurmjob.sh # job submitted by SLURM won't be restricted.
All in all, we just want to limit which tasks that are not submitted by SLURM. Thanks. ☺️
Here is a ad-hoc solution to your problem: https://unix.stackexchange.com/questions/526994/limit-resources-cpu-mem-only-in-ssh-session. The idea there is to constrain users in a cgroup whenever they are in an SSH session.
Other than that, there is a tool called Arbiter2 that was created for the purpose of controlling usage resources on login nodes.
Related
I want to run about 400 jobs in GCP-slurm from about 2,000 arrays.
The slurm settings and slurm.config settings in my bash file are as follows.
run.sh
#SBATCH -o ./out/vs.%j.out
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH -W
slurm.config
MaxArraySize=50000
MaxJobCount=50000
#COMPUTE NODE
NodeName=DEFAULT CPUs=16 RealMemory=63216 State=UNKNOWN
NodeName=node-0-[0-599] State=CLOUD
Currently, 100 nodes are being used for work other than that task.
If you proceed with this task, only about 130-150 node tasks in total are executed and the rest are not executed.
Are there any additional parameters that need to be set?
-- additional error log
[2022-06-20T01:18:41.294] error: get_addr_info: getaddrinfo() failed: Name or service not known
[2022-06-20T01:18:41.294] error: slurm_set_addr: Unable to resolve “node-333"
[2022-06-20T01:18:41.294] error: fwd_tree_thread: can't find address for host node-333, check slurm.conf
I found a workaround for the additional error.
https://groups.google.com/g/slurm-users/c/y-QZKDbYfIk
I referred to the article, and you can edit slurcmtld.service / slurmd.service / slurmdbd.service.
network.target -> network-online.target
However, the maximum number of execution nodes is still maintained.
I am new to Slurm and I also found the related questions about this topic. However, I am still confused about several points of how to use srun. According to the official document, srun will typically first allocate resources and then run the parallel jobs. For example, I want to run 20 tasks and if I submit my job based on the following script, I am not sure how many tasks are created. Because sbatch only takes care of allocating resources instead of executing program.
#!/bin/sh
#SBATCH -n 20
#SBATCH --mpi=pmi2
#SBATCH -o myoutputfile.txt
module load mpi/mpich-x86_64
mpirun mpiprogram < inputfile.txt
If I am trying to run sequential program like the following, I am not whether there will be a difference or not. For example, I can simply remove the srun command in this script. What will happen?
#!/bin/sh
#SBATCH -n 1
#SBATCH -N 1
srun tar zxf julia-0.3.11.tar.gz
echo "prefix=/software/julia-0.3.11" > julia/Make.user
cd julia
srun make
The first example will spawn 20 tasks ; sbatch will request 20 CPUs and also set up the environment so that mpirun knows how many CPUs were requested for the job. mpirun will then spawn as many processes as were allocated (provided that OpenMPI was compiled with Slurm support).
The #SBATCH --mpi=pmi2 part is meant for srun so it will have no effect if srun is not called in the submission script.
In the second example, there will be no difference in the number of processes spawned as only one is needed. But, with srun, the output of sstat will be more reliable, the management of signals will be more precise, and the buffering of the output will be more controlled (via the srun command line options).
If you request multiple tasks, srun will instantiate that many processes. It can be an MPI program, or a sequential program that adapts its behaviour based on the SLURM_PROC_ID environment variable.
Also you can run multiple srun in the same submission script. Each instance of srun (called a "step") is then accounted separately in the accounting (sacct).
Finally, srun can use a subset of the allocation and organise the micro-scheduling of many small tasks in a single job (see the example in the srun manpage).
I've installed Slurm on a 2-node cluster. Both nodes are compute nodes, one is the controller also. I am able to successfully run srun with multiple jobs at once. I am running GPU jobs and have confirmed I can get multiple jobs running on multiple GPUs with srun, up to the number of GPUs in the systems.
However, when I try running sbatch with the same test file, it will only run one batch job, and it only runs on the compute node which is also the controller. The others fail, with an ExitCode of 1:0 in the sacct summary. If I try forcing it to run on the compute node that's not the controller, it won't run and shows the 1:0 exit code. However, just using srun will run on any compute node.
I've made sure the /etc/slurm/slurm.conf files are correct with the specs of the machines. Here is the sbatch .job file I am using:
#!/bin/bash
#SBATCH --job-name=tf_test1
#SBATCH --output=/storage/test.out
#SBATCH --error=/storage/test.err
#SBATCH --ntasks=2
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=2000
##SBATCH --mem=10gb
#SBATCH --gres=gpu:1
~/anaconda3/bin/python /storage/tf_test.py
Maybe there is some limitation with sbatch I don't know about?
sbatch creates a job allocation and launches what is called the 'batch step'.
If you aren't familiar with what a job step is, I recommend this page: https://slurm.schedmd.com/quickstart.html
The batch step runs the script passed to it from sbatch. The only way to launch additional job steps is to invoke srun inside the batch step. In your case, it would be
srun ~/anaconda3/bin/python /storage/tf_test.py
This will create a job step running tf_test.py on each task in the allocation. Note that while the command is the same as when you run srun directly, it detects that is inside an allocation via environment variables from sbatch. You can split up the allocation into multiple job steps by running srun with flags like -n[num tasks] instead. ie
#!/bin/bash
#SBATCH --ntasks=2
srun --ntasks=1 something.py
srun --ntasks=1 somethingelse.py
I don't know if you're having any other problems because you didn't post any other error messages or logs.
If using srun on the second node works and using sbatch with the submission script you mention fails without any output written, the most probable reason would be that /storage does not exist, or is not writable by the user, on the second node.
The slurmd logs on the second node should be explicit about this. The default location is /var/log/slurm/slurmd.log, but check the output of scontrol show config| grep Log for definitive information.
Another probable cause that lead to the same behaviour would be that the user is not defined or has a different UID on the second node (but then srun would fail too)
#damienfrancois answer was closest and maybe even correct. After making sure the /storage location was available on all nodes, things run with sbatch. The biggest issue was the /storage location is shared via NFS, but it was read-only for the compute nodes. This had to be changed in /etc/exports to look more like:
/storage *(rw,sync,no_root_squash)
Before it was ro...
The job file I have that works is also a bit different. Here is the current .job file:
#!/bin/bash
#SBATCH -N 1 # nodes requested
#SBATCH --job-name=test
#SBATCH --output=/storage/test.out
#SBATCH --error=/storage/test.err
#SBATCH --time=2-00:00
#SBATCH --mem=36000
#SBATCH --qos=normal
#SBATCH --mail-type=ALL
#SBATCH --mail-user=$USER#nothing.com
#SBATCH --gres=gpu
srun ~/anaconda3/bin/python /storage/tf_test.py
I'm using slurm on a cluster where single partitions have dissimilar nodes. Specifically, the nodes have varying # CPUs. My code is a single-core application being used for a parameter sweep and thus I want to fully use an (eg.) 32 CPU node by sending it 32 jobs.
How can I select nodes (within a named partition) that have a specified number of CPUs?
I know my Partition configuration via
sinfo -e -p <partition_name> -o "%9P %3c %.5D %6t " -t idle,mix
PARTITION CPU NODES STATE
<partition_name> 16 63 mix
<partition_name> 32 164 mix
But if I use a submissions script like
[snip preamble]
#SBATCH --partition <partition_name> # resource to be used
#SBATCH --nodes 1 # Num nodes
#SBATCH -N 1 # Num cores per job
#SBATCH --cores-per-socket=32 # Cores per node
the slurm scheduler says
sbatch: error: Socket, core and/or thread specification can not be satisfied
PS. A minor correction: my code to get partition info isn't the best. Just in case anyone looks up this question later, here is a better query (using X,Y for socket, core counts) that helps identify the problem that damien's excellent answer solved
sinfo -e -p <partition_name> -o "%9P %3c %.3D %6t %2X %2Y %N" -t idle,mix
To strictly answer your question: With
#SBATCH --cores-per-socket=32
you request 32 core per socket, which is per physical CPU. I guess those machines have two CPUs so you should request something like
#SBATCH --sockets-per-node=2
#SBATCH --cores-per-socket=16
Another way of requesting the same is to ask for
#SBATCH --nodes 1
#SBATCH --tasks-per-node 32
But please note that, if your cluster allows node sharing, what you do seems more suited for job arrays :
#SBATCH --ntasks 1
#SBATCH --arrays 1-32
IDS=($(seq RUN_ID_FIRST RUN_ID_LAST))
RUN_ID=${IDS[$SLURM_ARRAY_TASK_ID]}
matlab -nojvm -singleCompThread -r "try myscript(${RUN_ID}); catch me; disp(' *** error'); end; exit" > ./result_${RUN_ID}
This will launch 32 independent jobs, each taking care of running the Matlab script for one value of the parameter sweep.
To answer your additional question; if a 32-process job is scheduled on a 16-CPU node, the node will be overloaded, and depending on the containment solution set up by the administrators, your processes might impact others' jobs and slow them down.
I am new to Parallel computing, I cannot understand the use of PBS systems. I have successfully install SLURM and set up processing nodes. But cannot get the idea how I can distribute a task between multiple nodes.
There are a lot of simple examples, but they just run simple "Hello World" programs and that's all.
Consider the following example, I've found on the internet.
#!/bin/bash
#SBATCH -N 4
#SBATCH -c 1
#SBATCH --time=0-00:15:00 # 30 minutes
#SBATCH --job-name="just_a_test"
module load python
python --version
Simple script that run gets the Python version.
When I run it using sbatch python.slurm, the result is saved only on the first node even if I set the number to 4. But srun -N4 /bin/hostname works fine on the other hand.
But this is not the main question.
I cannot understand what I have to write my parallel algorithm.
Any example of parallel algorithm like array sorting, matrix multiplication or whatever.
The steps that are used for example in Hadoop or just multithreaded environment.
Get input from a source.
Divide the input into chunks,the number of chunks should be related to the node count.
Send these chunk to each processing node/thread
Wait for all threads to complete
Gather processed information and show it user after merging
How can I do the same using SLURM or any PBS.
#!/bin/bash
#SBATCH -N 4
#SBATCH -c 1
#SBATCH --time=0-00:15:00 # 30 minutes
#SBATCH --job-name="just_a_test"
what I have to write here ?
Please explain this or give a good article to read about, because I haven't found any.
Thanks
The most basic way to do this is to use pbsdsh:
pbsdsh hostname
will make the hostname command execute once for each execution slot (core or thread) in your job. I will also point out that you'll need to translate your #SBATCH to their #PBS equivalents.
The more universal way to do this is through MPI implementations.