Untested, but prior experience with cgroups indicates that if things are
working correctly, even if your code tries to run as many processes as you have
cores, those processes will be confined to the cores you reserve.
Try a more compute-intensive worker function that will take some seconds or
minutes to complete, and watch the reserved node with 'top' or a similar
program. If for example, the job reserved only 1 core and tried to run 20
processes, you'd see 20 processes in 'top', each at 5% CPU time.
To make the code a bit more polite, you can import the os module and create a
new variable from the SLURM_CPUS_ON_NODE environment variable to guide Python
into starting the correct number of processes:
cpus_reserved = int(os.environ['SLURM_CPUS_ON_NODE'])
From: slurm-users <[email protected]> on behalf of Rodrigo
Santibáñez <[email protected]>
Date: Friday, May 14, 2021 at 5:17 PM
To: Slurm User Community List <[email protected]>
Subject: Re: [slurm-users] Exposing only requested CPUs to a job on a given
node.
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________________________________
Hi you all,
I'm replying to have notifications answering this question. I have a user whose
python script used almost all CPUs, but configured to use only 6 cpus per task.
I reviewed the code, and it doesn't have an explicit call to multiprocessing or
similar. So the user is unaware of this behavior (and also me).
Running slurm 20.02.6
Best!
On Fri, May 14, 2021 at 1:37 PM Luis R. Torres
<[email protected]<mailto:[email protected]>> wrote:
Hi Folks,
We are currently running on SLURM 20.11.6 with cgroups constraints for memory
and CPU/Core. Can the scheduler only expose the requested number of CPU/Core
resources to a job? We have some users that employ python scripts with the
multi processing modules, and the scripts apparently use all of the CPU/Cores
in a node, despite using options to constraint a task to just a given number of
CPUs. We would like several multiprocessing jobs to run simultaneously on
the nodes, but not step on each other.
The sample script I use for testing is below; I'm looking for something similar
to what can be done with the GPU Gres configuration where only the number of
GPUs requested are exposed to the job requesting them.
#!/usr/bin/env python3
import multiprocessing
def worker():
print("Worker on CPU #%s" % multiprocessing.current_process
().name)
result=0
for j in range(20):
result += j**2
print ("Result on CPU {} is {}".format(multiprocessing.curr
ent_process().name,result))
return
if __name__ == '__main__':
pool = multiprocessing.Pool()
jobs = []
print ("This host exposed {} CPUs".format(multiprocessing.c
pu_count()))
for i in range(multiprocessing.cpu_count()):
p = multiprocessing.Process(target=worker, name=i).star
t()
Thanks,
--
----------------------------------------
Luis R. Torres
