How do you stop numpy from multithreading? [duplicate]

Question

I have to run jobs on a regular basis on compute servers that I share with others in the department and when I start 10 jobs, I really would like it to just take 10 cores and not more; I don't care if it takes a bit longer with a single core per run: I just don't want it to encroach on the others' territory, which would require me to renice the jobs and so on. I just want to have 10 solid cores and that's all.

I am using Enthought 7.3-1 on Redhat, which is based on Python 2.7.3 and numpy 1.6.1, but the question is more general.

Answer 1

Only hopefully this fixes all scenarios and system you may be on.

1. Use numpy.__config__.show() to see if you are using OpenBLAS or MKL

From this point on there are a few ways you can do this.

2.1. The terminal route export OPENBLAS_NUM_THREADS=1 or export MKL_NUM_THREADS=1

2.2 (This is my preferred way) In your python script import os and add the line os.environ['OPENBLAS_NUM_THREADS'] = '1' or os.environ['MKL_NUM_THREADS'] = '1'.

NOTE when setting os.environ[VAR] the number of threads must be a string! Also, you may need to set this environment variable before importing numpy/scipy.

There are probably other options besides openBLAS or MKL but step 1 will help you figure that out.