How do you stop numpy from multithreading?
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.
Answers:
Set the MKL_NUM_THREADS environment variable to 1. As you might have guessed, this environment variable controls the behavior of the Math Kernel Library which is included as part of Enthought’s numpy build.
I just do this in my startup file, .bash_profile, with export MKL_NUM_THREADS=1. You should also be able to do it from inside your script to have it be process specific.
In more recent versions of numpy I have found it necessary to also set NUMEXPR_NUM_THREADS=1.
In my hands, this is sufficient without setting MKL_NUM_THREADS=1, but under some circumstances you may need to set both.
For me, the solution was simple as I stopped using numpy.dot:
import numpy as np
a = np.random.rand(1e6)
b = np.random.rand(1e6, 10)
# potentially uses multiple threads
dotted = np.dot(a, b)
# single-thread
summed = np.sum(a[:, np.newaxis] * b, axis=0)
assert np.all(dotted == summed)
In case you want to set the number of threads dynamically, and not globally via an environment variable, you can also do:
import mkl
mkl.set_num_threads(2)
Only hopefully this fixes all scenarios and system you may be on.
- 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.
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.
Set the MKL_NUM_THREADS environment variable to 1. As you might have guessed, this environment variable controls the behavior of the Math Kernel Library which is included as part of Enthought’s numpy build.
I just do this in my startup file, .bash_profile, with export MKL_NUM_THREADS=1. You should also be able to do it from inside your script to have it be process specific.
In more recent versions of numpy I have found it necessary to also set NUMEXPR_NUM_THREADS=1.
In my hands, this is sufficient without setting MKL_NUM_THREADS=1, but under some circumstances you may need to set both.
For me, the solution was simple as I stopped using numpy.dot:
import numpy as np
a = np.random.rand(1e6)
b = np.random.rand(1e6, 10)
# potentially uses multiple threads
dotted = np.dot(a, b)
# single-thread
summed = np.sum(a[:, np.newaxis] * b, axis=0)
assert np.all(dotted == summed)
In case you want to set the number of threads dynamically, and not globally via an environment variable, you can also do:
import mkl
mkl.set_num_threads(2)
Only hopefully this fixes all scenarios and system you may be on.
- 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.