Inspecting or turning off Numpy/SciPy Parallelization
Indeed BLAS, or in my case OpenBLAS, was performing the parallelization.
The solution was to set the environment variable OMP_NUM_THREADS to 1.
Then all is right with the world.
I am running some K-Means clustering from the sklearn package.
Although I am setting the parameter n_jobs = 1 as indicated in the sklearn documentation, and  although a single process is running, that process will apparently consume all the CPUs on my machine. That is, in top, I can see the python job is using, say 400% on a 4 core machine.
To be clear, if I set n_jobs = 2, say, then I get two python instances running, but each one uses 200% CPU, again consuming all 4 of my machine's cores.
I believe the issue may be parallelization at the level of NumPy/SciPy.
Is there a way to verify my assumption? Is there a way to turn off any parallelization in NumPy/SciPy, for example?