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Intel(R) Extension for Scikit-learn 2021.2

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@PetrovKP PetrovKP released this 30 Mar 20:46
· 1 commit to rls/2021.2.1-rls since this release
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⚡️ New package - Intel(R) Extension for Scikit-learn*

  • Intel(R) Extension for Scikit-learn* contains scikit-learn patching functionality originally available in daal4py package. All future updates for the patching will be available in Intel(R) Extension for Scikit-learn only. Please use the package instead of daal4py.

⚠️ Deprecations

  • Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package - Intel(R) Extension for Scikit-learn*. All future updates for the patching will be available in Intel(R) Extension for Scikit-learn only. Please use the package instead of daal4py for the Scikit-learn acceleration.

📚 Support Materials

🛠️ Library Engineering

  • Enabled new PyPI distribution channel for Intel(R) Extension for Scikit-learn and daal4py:
    • Four latest Python versions (3.6, 3.7, 3.8) are supported on Linux, Windows and MacOS.
    • Support of both CPU and GPU is included in the package.
    • You can download daal4py using the following command: pip install daal4py
    • You can download Intel(R) Extension for Scikit-learn using the following command: pip install scikit-learn-intelex

🚨 New Features

  • Patches for four latest scikit-learn releases: 0.21.X, 0.22.X, 0.23.X and 0.24.X
  • [CPU] Acceleration of roc_auc_score function
  • [CPU] Bit-to-bit results reproducibility for: LinearRegression, Ridge, SVC, KMeans, PCA, Lasso, ElasticNet, tSNE, KNeighborsClassifier, KNeighborsRegressor, NearestNeighbors, RandomForestClassifier, RandomForestRegressor

🚀 ​Improved performance

  • [CPU] RandomForestClassifier and RandomForestRegressor scikit-learn estimators: training and prediction
  • [CPU] Principal Component Analysis (PCA) scikit-learn estimator: training
  • [CPU] Support Vector Classification (SVC) scikit-learn estimators: training and prediction
  • [CPU] Support Vector Classification (SVC) scikit-learn estimator with the probability==True parameter: training and prediction

🐛 Bug Fixes

  • [CPU] Improved accuracy of RandomForestClassifier and RandomForestRegressor scikit-learn estimators
  • [CPU] Fixed patching issues with pairwise_distances
  • [CPU] Fixed the behavior of the patch_sklearn and unpatch_sklearn functions
  • [CPU] Fixed unexpected behavior that made accelerated functionality unavailable through scikit-learn patching if the input was not of float32 or float64 data types. Scikit-learn patching now works with all numpy data types.
  • [CPU] Fixed a memory leak that appeared when DataFrame from pandas was used as an input type
  • [CPU] Fixed performance issue for interoperability with Modin