Does partial fit runs in parallel in sklearn.decomposition.IncrementalPCA?

Question

I've followed Imanol Luengo's answer to build a partial fit and transform for sklearn.decomposition.IncrementalPCA. But for some reason, it looks like (from htop) it uses all CPU cores at maximum. I could find neither n_jobs parameter nor anything related to multiprocessing. My question is: if this is default behavior of these functions how can I set the number of CPU's and where can I find information about it? If not, obviously I am doing something wrong in previous sections of my code.

PS: I need to limit the number of CPU cores because using all cores in a server causing a lot of trouble with other people.

Additional information and debug code: So, it has been a while and I still couldn't figure out the reason for this behavior or how to limit the number of CPU cores used at a time. I've decided to provide a sample code to test it. Note that, this code snippet is taken from the sklearn's website. The only difference is made to increase the size of the dataset, so one can easily see the behavior.

from sklearn.datasets import load_digits
from sklearn.decomposition import IncrementalPCA
import numpy as np

X, _ = load_digits(return_X_y=True)

#Copy-paste and increase the size of the dataset to see the behavior at htop.
for _ in range(8):
    X = np.vstack((X, X))

print(X.shape)

transformer = IncrementalPCA(n_components=7, batch_size=200)
transformer.partial_fit(X[:100, :])
X_transformed = transformer.fit_transform(X)

print(X_transformed.shape)

And the output is:

(460032, 64)
(460032, 7)

Process finished with exit code 0

And the htop shows:

Answer 1

I was looking for a workaround to this problem in another post of mine and I figured out this is not because of scikit-learn implementation fault but rather due to BLAS library (specifically OpenBLAS) used by numpy library, which is used in sklearn's IncrementalPCA function. OpenBLAS is set to use all available threads by default. Detailed information can be found here.

TL:DR Solved the issue by setting BLAS environmental variables before importing numpy or any library that imports numpy with the code below. Detailed information can be found here.

import os
os.environ["OMP_NUM_THREADS"] = 1 # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = 1 # export OPENBLAS_NUM_THREADS=1
os.environ["MKL_NUM_THREADS"] = 1 # export MKL_NUM_THREADS=1
os.environ["VECLIB_MAXIMUM_THREADS"] = 1 # export VECLIB_MAXIMUM_THREADS=1
os.environ["NUMEXPR_NUM_THREADS"] = 1 # export NUMEXPR_NUM_THREADS=1