Intel® oneAPI Data Analytics Library 2021.3

[KalyanovD]

The release introduces the following changes:

📚 Support Materials

The following additional materials were created:

• 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
  • Optimizing the End-to-End Training Pipeline on Apache Spark Clusters

• 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 scikit-learn-intelex

• Samples that illustrate the usage of Intel Extension for Scikit-learn

🛠️ Library Engineering

• Introduced a new Python package, Intel® Extension for Scikit-learn*. The scikit-learn-intelex package contains scikit-learn patching functionality that was originally available in daal4py package. All future updates for the patches will be available only in Intel® Extension for Scikit-learn. We recommend using scikit-learn-intelex package instead of daal4py.
  • Download the extension using one of the following commands:
    • pip install scikit-learn-intelex
    • conda install scikit-learn-intelex -c conda-forge
  • Enable Scikit-learn patching:
    • from sklearnex import patch_sklearn
    • patch_sklearn()
• Introduced optional dependencies on DPC++ runtime to 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.
• Added the support of building oneDAL-based applications with /MD and /MDd options on Windows. The -d suffix is used in the names of oneDAL libraries that are built with debug run-time (/MDd).

🚨 What's New

• Introduced new oneDAL and daal4py functionality:
  • CPU:
    • SVM Regression algorithm
    • NuSVM algorithm for both Classification and Regression tasks
    • Polynomial kernel support for all SVM algorithms (SVC, SVR, NuSVC, NuSVR)
    • Minkowski and Chebyshev distances for kNN Brute-force
    • The brute-force method and the voting mode support for kNN algorithm in oneDAL interfaces
    • Multiclass support for SVM algorithms in oneDAL interfaces
    • CSR-matrix support for SVM algorithms in oneDAL interfaces
    • Subgraph Isomorphism algorithm technical preview
    • Single Source Shortest Path (SSSP) algorithm technical preview
• Improved oneDAL and daal4py performance for the following algorithms:
  • CPU:
    • Support Vector Machines training and prediction
    • Linear, Ridge, ElasticNet, and LASSO regressions prediction
  • GPU:
    • Decision Forest training and prediction
    • Principal Components Analysis training
• Introduced the support of scikit-learn 1.0 version in Intel Extension for Scikit-learn. The 2021.3 release of Intel Extension for Scikit-learn supports the latest scikit-learn releases: 0.22.X, 0.23.X, 0.24.X and 1.0.X.
• Introduced new functionality for Intel Extension for Scikit-learn:
  • General:
    • The support of patch_sklearn for all algorithms
  • CPU:
    • Acceleration of SVR estimator
    • Acceleration of NuSVC and NuSVR estimators
    • Polynomial kernel support in SVM algorithms
• Improved the performance of the following scikit-learn estimators via scikit-learn patching:
  • SVM algorithms training and prediction
  • Linear, Ridge, ElasticNet, and Lasso regressions prediction
• Fixed the following issues:
  • General:
    • 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:
    • Improved numerical stability of training for Alternating Least Squares (ALS) and Linear and Ridge regressions with Normal Equations method
    • Reduced the memory consumption of SVM prediction
  • GPU:
    • Fixed an issue with kernel compilation on the platforms without hardware FP64 support

❗ Known Issues

• Intel® Extension for Scikit-learn and daal4py packages 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")) 

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This discussion was created from the release Intel® oneAPI Data Analytics Library 2021.3.