Intel(R) Extension for Scikit-learn 2021.3
The release Intel(R) Extension for Scikit-learn 2021.3 introduces the following changes:
📚 Support Materials
- Medium blogs:
- Kaggle kernels:
- [Tabular Playground Series - Apr 2021] RF with Intel Extension for Scikit-learn
- [Tabular Playground Series - Apr 2021] SVM with Intel Extension for Scikit-learn
- [Tabular Playground Series - Apr 2021] SVM with Intel(R) Extension for Scikit-learn
- [Tabular Playground Series - Jun 2021] AutoGluon with Intel(R) Extension for Scikit-learn
- [Tabular Playground Series - Jun 2021] Fast LogReg with Intel(R) Extension for Scikit-learn
- [Tabular Playground Series - Jun 2021] Fast ML stack with Intel(R) Extension for Scikit-learn
- [Tabular Playground Series - Jun 2021] Fast Stacking with Intel(R) Extension for Scikit-learn
- Samples that illustrate the usage of Intel Extension for Scikit-learn
🛠️ Library Engineering
- Introduced optional dependencies on DPC++ runtime to Intel Extension for Scikit-learn and daal4py. To enable DPC++ backend, install dpcpp_cpp_rt package. It reduces the default package size with all dependencies from 1.2GB to 400 MB.
🚨 New Features
- Introduced the support of scikit-learn 1.0 version in Intel(R) Extension for Scikit-learn. The 2021.3 release of Intel(R) Extension for Scikit-learn supports the latest scikit-learn releases: 0.22.X, 0.23.X, 0.24.X and 1.0.X.
- The support of
patch_sklearn
for several algorithms: patch_sklearn(["SVC", "DBSCAN"]) - [CPU] Acceleration of
SVR
estimator - [CPU] Acceleration of
NuSVC
andNuSVR
estimators - [CPU]
Polynomial kernel
support in SVM algorithms
🚀 Improved performance
- [CPU]
SVM
algorithms training and prediction - [CPU]
Linear
,Ridge
,ElasticNet
, andLasso
regressions prediction
🐛 Bug Fixes
- Fixed binary incompatibility for the versions of numpy earlier than 1.19.4
- Fixed an issue with a very large number of trees (> 7000) for
Random Forest
algorithm - Fixed
patch_sklearn
to patch both fit and predict methods ofLogistic Regression
when the algorithm is given as a single parameter topatch_sklearn
- [CPU] Reduced the memory consumption of
SVM
prediction - [GPU] Fixed an issue with kernel compilation on the platforms without hardware FP64 support
❗ Known Issues
- Intel(R) Extension for Scikit-learn package installed from PyPI repository can’t be found on Debian systems (including Google Collab). Mitigation: add “site-packages” folder into Python packages searching before importing the packages:
import sys
import os
import site
sys.path.append(os.path.join(os.path.dirname(site.getsitepackages()[0]), "site-packages"))