Global Patching

Use global patching to patch all your scikit-learn applications without any additional actions.

Prerequisites

  • Intel® Extension for Scikit-learn*

  • Scikit-learn

  • read and write permissions to Scikit-learn files

Patch all supported algorithms

To patch all supported algorithms, run:

python sklearnex.glob patch_sklearn

Patch selected algorithms

If you want to patch only some algorithms, use --algorithm or -a keys with a list of algorithms to patch.

For example, to patch only SVC and RandomForestClassifier estimators, run:

python sklearnex.glob patch_sklearn -a svc random_forest_classifier

Disable patching notifications

If you do not want to receive patching notifications, then use --no-verbose or -nv keys:

python sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv

Note

If you run the global patching command several times with different parameters, then only the last configuration will be applied.

Disable global patching

To disable global patching, use the following command:

python sklearnex.glob unpatch_sklearn

Enable global patching via code

You can also enable global patching in your code. To do this, use the patch_sklearn function with the global_patch argument:

from sklearnex import patch_sklearn
patch_sklearn(global_patch=True)
import sklearn

After that, Scikit-learn patches will be enabled in the current application and in all others that use the same environment.

Disable global patching via code

To disable global patching via code, use the global_patch argument in the unpatch_sklearn function:

from sklearnex import unpatch_sklearn
unpatch_sklearn(global_patch=True)

Note

If you clone an environment with enabled global patching, it will already be applied in the new environment.