scikit-learn-intelex 2021.3.0

Intel(R) Extension for Scikit-learn 2021.3

on Python PyPI

The release Intel(R) Extension for Scikit-learn 2021.3 introduces the following changes:

📚 Support Materials

• Medium blogs:
  • Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors
  • Leverage Intel Optimizations in Scikit-Learn (SVM Performance Training and Inference)
  • Optimizing CatBoost Performance
  • Performance Optimizations for End-to-End AI Pipelines
• 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 and NuSVR estimators
• [CPU] Polynomial kernel support in SVM algorithms

🚀 ​Improved performance

• [CPU] SVM algorithms training and prediction
• [CPU] Linear, Ridge, ElasticNet, and Lasso 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 of Logistic Regression when the algorithm is given as a single parameter to patch_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"))