Patching¶
- patching¶
To patch scikit-learn with Intel® Extension for Scikit-learn* is to replace stock scikit-learn algorithms with their optimized versions provided by the extension. You can always undo the patch.
There are different ways to patch scikit-learn:
Important
These patching methods are interchangeable. They support different enabling scenarios while producing the same result.
Without editing the code of a scikit-learn application by using the following command line flag:
python -m sklearnex my_application.py
Directly from the script:
from sklearnex import patch_sklearn patch_sklearn()
Important
You have to import scikit-learn after these lines. Otherwise, the patching will not affect the original scikit-learn estimators.
Through importing the desired estimator from the sklearnex module in your script:
from sklearnex.neighbors import NearestNeighbors
Through global patching to enable patching for your scikit-learn installation for all further runs:
python sklearnex.glob patch_sklearn
See also
- global pathcing¶
One of the patching options available in Intel® Extension for Scikit-learn*. With global patching, you can patch all scikit-learn applications at once:
python sklearnex.glob patch_sklearn
See also
- unpatching¶
To undo the patch is to return to the use of original scikit-learn implementation and replace patched algorithms with the stock scikit-learn algorithms. Unpatching requires scikit-learn to be re-imported again:
sklearnex.unpatch_sklearn() # Re-import scikit-learn algorithms after the unpatch: from sklearn.cluster import KMeans
How it works¶
The extension replaces the original estimators in scikit-learn modules with the optimized ones. If the desired algorithm parameters are not supported by the Intel® Extension for Scikit-learn*, then the result of the original scikit-learn is returned.
See also