memory leak using SVR

[montagne5641]

When using SVR with patch, a memory leak occurs every time an assignment is made repeatedly with for.
This does not occur if patch is not used.
Similar issues have been reported previously with SVC, and daal4py was suggested as an alternative at the time. However, SVR does not support daal4py.
Is it possible to address the memory leak?

#-----------------------------------------------------
use_intelex =1

if use_intelex==1:
from sklearnex import patch_sklearn
patch_sklearn()

from sklearn.svm import SVR
import numpy as np

X = np.random.randint(10, size=(1000,500))
y = np.random.randint(2, size=(1000))

for i in range(10000):
results = SVR().fit(X,y)
#-----------------------------------------------------

[montagne5641]

This issue was not resolved by using del and go.collect()....
I think this problem becomes even more pronounced when used in combination with gridsearchcv etc.

---

import gc

use_intelex =1

if use_intelex==1:
from sklearnex import patch_sklearn
patch_sklearn()

import os
from sklearn.svm import SVR
import numpy as np

X = np.random.randint(10, size=(1000,500))
y = np.random.randint(2, size=(1000))

for i in range(10000):
results = SVR().fit(X,y)
del results
gc.collect()

[samir-nasibli]

Thank you @montagne5641 for the report!
In order to reproduce the issues, please share the version of daal4py and scikit-learn-intelex and more details about your env

[montagne5641]

dear samir-nasibli
Thank you. This is my environment. If you need any other information, I'll be happy to help.

daal4py==2024.4.0
scikit-learn-intelex==2024.4.0
scikit-learn==1.5.0
Windows 10 Pro

Note: The same issue occurred even when I lowered the version as shown below.
daal4py-2023.1.1
scikit-learn-intelex-2023.1.1

[montagne5641]

Any update on this?

[jamescporter]

Same here, any updates?

[jamescporter]

This issue was not resolved by using del and go.collect().... I think this problem becomes even more pronounced when used in combination with gridsearchcv etc.

import gc

use_intelex =1

if use_intelex==1: from sklearnex import patch_sklearn patch_sklearn()

import os from sklearn.svm import SVR import numpy as np

X = np.random.randint(10, size=(1000,500)) y = np.random.randint(2, size=(1000))

for i in range(10000):

results = SVR().fit(X,y)
del results
gc.collect()

did you find that you would always run out of memory doing a grid search using this?