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 <lrtor...@gmail.com> 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
>
