Intel® Extension for Scikit-learn 2023.2.0

The release of Intel® Extension for Scikit-learn 2023.2.0 introduces the following changes:

❌ Deprecation Notice

• The compression functionality in the Intel® oneDAL library is deprecated. Starting with the 2024.0 release, oneDAL will not support the compression functionality
• The DAAL CPP SYCL Interfaces in the Intel® oneDAL library are deprecated. Starting with the 2024.0 release, oneDAL will not support the DAAL CPP SYCL Interfaces
• The Java* interfaces in the Intel® oneDAL library are marked as deprecated. The future releases of the oneDAL library may no longer include support for these Java* interfaces
• ABI compatibility is to be broken as part of the 2024.0 release of Intel® oneDAL. The library’s major version is to be incremented to two to enforce the relinking of existing applications
• macOS* support is deprecated for oneDAL. The 2023.x releases are the last to provide it

🛠️ Library Engineering

• CSR tables interface has been changed and moved from detail namespace

🚨 What's New

• Introduced new Intel® oneDAL functionality:
  • Distributed KMeans++ algorithm
  • Logistic Loss objective algorithm
• Introduced new functionality for Intel® Extension for Scikit-learn:
  • NaN(missing values) support was added to Model Builders
• Improved performance for the following Intel® Extension for Scikit-learn algorithms:
  • Model Builders performance has been improved up to 2x

Intel® Extension for Scikit-learn 2023.1.1

The release of Intel® Extension for Scikit-learn 2023.1.1 introduces the following changes:

🚨 What's New

• Splitting mode for Random Forest algorithm 4abff0d
• SPMD interface for Random Forest, kNN and PCA e887d99, 97f0d47, 2405630
• Native support for DPCTL ca28499
• Patching for sklearn's LocalOutlierFactor 7ae0171

Intel® Extension for Scikit-learn 2023.1.0

The release of Intel® Extension for Scikit-learn 2023.1 introduces the following changes:

📚Support Materials

• Accelerating Barnes-Hut t-SNE Algorithm by Efficient Parallelization on Multi-Core CPUs

🛠️ Library Engineering

• Reduced the size of Intel® oneDAL library by approximately ~30%

🚨 What's New

• Introduced new functionality for Intel® Extension for Scikit-learn:
  • Enabled PCA, Linear Regression, Random Forest algorithms and SPMD policy as preview
  • Scikit-learn 1.2 support
  • sklearn_is_patched() function added to validate status of algorithms patching
• Improved performance for the following Intel® Extension for Scikit-learn algorithms:
  • t-SNE for “Burnes-Hut” algorithm
  • SVM algorithm for single row inference

❗ Known Issues

• In certain conditions DAAL SYCL interface might hang with L0 backend – please use oneDAL DPC interfaces instead. If older interfaces are required OpenCL backend can be used as workaround.

Intel® Extension for Scikit-learn 2023.0.1

The release of Intel® Extension for Scikit-learn 2023.0.1 introduces the following changes:

🚨 What's New

• Performance improvements for tSNE algorithm 5275eba
• Fixes for balanced classes and number of iterations in SVM 14849ee, 4872a8e, 9d0a05b
• Fix for gamma parameter in KMeans 1dca20c

Intel® Extension for Scikit-learn 2023.0.0

The release of Intel® Extension for Scikit-learn 2023.0 introduces the following changes:

🚨 What's New

• Introduced new Intel® oneDAL functionality:
  • DPC++ interface for Linear Regression algorithm

❗ Known Issues

• Intel® Extension for Scikit-learn SVC.fit and KNN.fit do not support GPU
• Most Intel® Extension for Scikit-learn sycl examples fail when using GPU context
• Running the Random Forest algorithm with versions 2021.7.1 and 2023.0 of scikit-learn-intelex on the 2nd Generation Intel® Xeon® Scalable Processors, formerly Cascade Lake may result in an 'Illegal instruction' error.
  • No workaround is currently available for this issue.
  • Recommendation: Use an older version of scikit-learn-intelex until the issue is fixed in a future release.

Intel(R) Extension for Scikit-learn 2021.7.1

The release Intel® Extension for Scikit-learn 2021.7.1 introduces the following changes:

📚 Support Materials

• [Tabular Playground Series - Sep 2022] Tuning of ElasticNet hyperparameters
• Accelerated Random Forest for Rent Prediction

🚨 What's New

• oneAPI interface for kNN regression
• Fix for wrong column names of pandas DataFrame in sklearn.model_selection.train_test_split patched function

Intel(R) Extension for Scikit-learn 2021.6

The release Intel® Extension for Scikit-learn 2021.6 introduces the following changes:

📚 Support Materials

Kaggle kernels:

• Fast Feature Importance using scikit-learn-intelex
• [Tabular Playground Series - December 2021] Fast Feature Importance with sklearnex
• [Tabular Playground Series - December 2021] SVC with sklearnex 20x speedup
• [Tabular Playground Series - January 2022] Fast PyCaret with Scikit-learn-Intelex
• [Tabular Playground Series - February 2022] KNN with sklearnex 13x speedup
• Fast SVM for Sparse Data from NLP Problem
• Introduction to scikit-learn-intelex
• [Datasets] Fast Feature Importance using sklearnex
• [Tabular Playground Series - March 2022] Fast workflow using scikit-learn-intelex

🛠️ Library Engineering

• Reduced the size of oneDAL python run-time package by approximately 8%
• Added Python 3.10 support for daal4py and Intel(R) Extension for Scikit-learn packages

🚨 What's new

• Improved performance for the following Intel® Extension for Scikit-learn algorithms:

  • t-SNE for “Burnes-Hut” algorithm

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

  • Manhattan, Minkowski, Chebyshev and Cosine distances for KNeighborsClassifier and NearestNeighbors with “brute” algorithm

• Fixed the following issues in Intel® Extension for Scikit-learn:

  • An issue with the search of common data type in pandas DataFrame
  • Patching overhead of finiteness checker for specific small data sizes
  • Incorrect values in a tree visualization with plot_tree function in RandomForestClassifier
  • Unexpected error for device strings in {device}:{device_index} format while using config context

Intel(R) Extension for Scikit-learn 2021.5

The release Intel® Extension for Scikit-learn 2021.5 introduces the following changes:

📚 Support Materials

• Kaggle kernels:
  • [Tabular Playground Series - Sep 2021] Ridge with sklearn-intelex 2x speedup
  • [Tabular Playground Series - Oct 2021] Fast AutoML with Intel Extension for Scikit-learn
  • [Titanic – Machine Learning from Disaster] AutoML with Intel Extension for Sklearn
  • [Tabular Playground Series - Nov 2021] AutoML with Intel® Extension
  • [Tabular Playground Series - Nov 2021] Log Regression with sklearnex 17x speedup
  • [Tabular Playground Series - Dec 2021] SVC with sklearnex 20x speedup
  • [Tabular Playground Series - Dec 2021] Fast Feature Importance with sklearnex
• Added demo samples of the Intel® Extension for Scikit-learn usage with the performance comparison to original Scikit-learn for ElasticNet, K-means, Lasso Regression, Linear regression, and Ridge Regression
• Added demo samples of the Modin usage

🛠️ Library Engineering

• Reduced the size of oneDAL library by approximately ~15%, this is a required dependency of Intel® extension for scikit learn.

🚨 New Features

• Scikit-learn 1.0 support

🚀 ​Improved performance

• [GPU] KNN algorithm prediction
• [GPU] SVC and SVR algorithms training

🐛 Bug Fixes

• Stabilized the results of Linear Regression in oneDAL and Intel® Extension for Scikit-learn
• Fixed an issue with RPATH on MacOS

Intel(R) Extension for Scikit-learn 2021.4

The release Intel(R) Extension for Scikit-learn 2021.4 introduces the following changes:

📚 Support Materials

• 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 - Jun 2021] Fast LogReg with scikit-learn-intelex
  • [Tabular Playground Series - Jun 2021] AutoGluon with sklearnex
  • [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.

🚨 New Features

• 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 performance

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

🐛 Bug Fixes

• 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.
• [CPU] Fixed an issue in NuSVC Multiclass.
• [CPU] Fixed an issue with KMeans convergence inconsistency.
• [CPU] 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.
• [GPU] Examples are failing when run with Visual Studio Solutions on hardware that does not support double precision floating-point operations.

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:
  • 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"))