How to Limit NumPy Thread Utilization When OMP_NUM_THREADS=1 Fails?

Limiting the Number of Threads in NumPy

When you set OMP_NUM_THREADS=1 and yet find that Numpy is still utilizing multiple threads, a different approach is needed. It is likely that Numpy is using the Basic Linear Algebra Subprograms (BLAS) library for numerical computations, which may itself be utilizing multiple threads.

In order to limit the number of threads utilized by NumPy, you can set certain environment flags before executing your script. Try setting the following:

export MKL_NUM_THREADS=1
export NUMEXPR_NUM_THREADS=1
export OMP_NUM_THREADS=1

It is important to note that the exact source of multithreading within your code can sometimes be difficult to identify. Other libraries, as outlined by other responses, may also introduce multithreading. These environment flags may or may not be effective in all cases, but they are a good starting point.

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