My laptop has Intel I7 processor with 4 cores. When I run the program on
Windows 10, the "joblib.cpu_count()" routine returns "4". In these
cases, the same test I did on the Cray computer caused a 10% increase in
the processing time of the DBScan routine when I used the "n_jobs = 4"
parameter compared to the processing time of that routine without this
parameter. Do you know what is the cause of the longer processing time
when I use "n_jobs = 4" on my laptop?
---
Ats.,
Mauricio Reis
Em 28/06/2019 06:29, Brown J.B. via scikit-learn escreveu:
where you can see "ncpus = 1" (I still do not know why 4 lines were
printed -
(total of 40 nodes) and each node has 1 CPU and 1 GPU!
#PBS -l select=1:ncpus=8:mpiprocs=8
aprun -n 4 p.sh ./ncpus.py
You can request 8 CPUs from a job scheduler, but if each node the
script runs on contains only one virtual/physical core, then
cpu_count() will return 1.
If that CPU supports multi-threading, you would typically get 2.
For example, on my workstation:
`--> egrep "processor|model name|core id" /proc/cpuinfo
processor : 0
model name : Intel(R) Core(TM) i3-4160 CPU @ 3.60GHz
core id : 0
processor : 1
model name : Intel(R) Core(TM) i3-4160 CPU @ 3.60GHz
core id : 1
processor : 2
model name : Intel(R) Core(TM) i3-4160 CPU @ 3.60GHz
core id : 0
processor : 3
model name : Intel(R) Core(TM) i3-4160 CPU @ 3.60GHz
core id : 1
`--> python3 -c "from sklearn.externals import joblib;
print(joblib.cpu_count())"
4
It seems that in this situation, if you're wanting to parallelize
*independent* sklearn calculations (e.g., changing dataset or random
seed), you'll ask for the MPI by PBS processes like you have, but
you'll need to place the sklearn computations in a function and then
take care of distributing that function call across the MPI processes.
Then again, if the runs are independent, it's a lot easier to write a
for loop in a shell script that changes the dataset/seed and submits
it to the job scheduler to let the job handler take care of the
parallel distribution.
(I do this when performing 10+ independent runs of sklearn modeling,
where models use multiple threads during calculations; in my case,
SLURM then takes care of finding the available nodes to distribute the
work to.)
Hope this helps.
J.B.
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