Intel® oneAPI Data Analytics Library 2021.4

The release introduces the following changes:

📚 Support Materials

The following additional materials were created:

• Medium blogs:

  • Save Time and Money with Intel Extension for Scikit-learn

• Anaconda blogs:

  • Scikit-learn Speed-up with Intel and Anaconda

• Oracle blogs:

  • Accelerate your model build process with the Intel® Extension for Scikit-learn

• Kaggle kernels:

  • [Tabular Playground Series - Jul 2021] Fast RandomForest with sklearnex
  • [Tabular Playground Series - Jul 2021] RF with Intel Extension for Scikit-learn
  • [Tabular Playground Series - Jul 2021] Stacking with scikit-learn-intelex
  • [Tabular Playground Series - Aug 2021] NuSVR with Intel Extension for Sklearn
  • [Predict Future Sales] Stacking with scikit-learn-intelex
  • [House Prices - Advanced Regression Techniques] NuSVR sklearn-intelex 4x speedup

• Added demo samples comparing the usage of Intel® Extension for Scikit-learn and the original Scikit-learn for KNN, Logistic Regression, SVM and Random Forest algorithms

🛠️ Library Engineering

• Introduced new functionality for Intel® Extension for Scikit-learn*:
  • Enabled patching for all Scikit-learn applications at once:
    • You can enable global patching via command line:
      • python -m sklearnex.glob patch_sklearn
    • Or via code:
      • from sklearnex import patch_sklearn
patch_sklearn(global_patch=True)
    • Read more in Intel® Extension for Scikit-learn documentation.
  • Added the support of Python 3.9 for both Intel® Extension for Scikit-learn and daal4py. The packages are available from PyPI and the Intel Channel on Anaconda Cloud.
• Introduced new oneDAL functionality:
  • Added pkg-config support for Linux, macOS, Windows and for static/dynamic, thread/sequential configurations of oneDAL applications.
  • Reduced the size of oneDAL library by approximately ~30%.

🚨 What's New

Introduced new oneDAL functionality:

• General:
  • Basic statistics (Low order moments) algorithm in oneDAL interfaces
  • Result options for kNN Brute-force in oneDAL interfaces: using a single function call to return any combination of responses, indices, and distances
• CPU:
  • Sigmoid kernel of SVM algorithm
  • Model converter from CatBoost to oneDAL representation
  • Louvain Community Detection algorithm technical preview
  • Connected Components algorithm technical preview
  • Search task and cosine distance for kNN Brute-force
• GPU:
  • The full range support of Minkowski distances in kNN Brute-force

Improved oneDAL performance for the following algorithms:

• CPU:
  • Decision Forest training and prediction
  • Brute-force kNN
  • KMeans
  • NuSVMs and SVR training

Introduced new functionality in Intel® Extension for Scikit-learn:

• General:
  • Enabled the global patching of all Scikit-learn applications
  • Provided an integration with dpctl for heterogeneous computing (the support of dpctl.tensor.usm_ndarray for input and output)
  • Extended API with set_config and get_config methods. Added the support of target_offload and allow_fallback_to_host options for device offloading scenarios
  • Added the support of predict_proba in RandomForestClassifier estimator
• CPU:
  • Added the support of Sigmoid kernel in SVM algorithms
• GPU:
  • Added binary SVC support with Linear and RBF kernels

Improved the performance of the following scikit-learn estimators via scikit-learn patching:

• SVR algorithm training
• NuSVC and NuSVR algorithms training
• RandomForestRegression and RandomForestClassifier algorithms training and prediction
• KMeans

🐛 Bug Fixes

• General:
  • Fixed an incorrectly raised exception during the patching of Random Forest algorithm when the number of trees was more than 7000.
• CPU:
  • Fixed an accuracy issue in Random Forest algorithm caused by the exclusion of constant features.
  • Fixed an issue in NuSVC Multiclass.
  • Fixed an issue with KMeans convergence inconsistency.
  • Fixed incorrect work of train_test_split with specific subset sizes.
• GPU:
  • Fixed incorrect bias calculation in SVM.

❗ Known Issues

• GPU:
  • For most algorithms, performance degradations were observed when the 2021.4 version of Intel® oneAPI DPC++ Compiler was used.
  • Examples are failing when run with Visual Studio Solutions on hardware that does not support double precision floating-point operations.