https://github.com/uxlfoundation/scikit-learn-intelex - Extension for Scikit-learn* 2025.8.0

Extension for Scikit-learn* is happy to introduce 2025.8.0 release!

:rotating_light: What's New

• Introduced new Extension for Scikit-learn* functionality:
  • Enabled array API support in DBSCAN
  • Enabled array API support in BasicStatistics, LinearRegression, Ridge algorithms and their incremental variants
  • Added parameters for covariance and PCA controlling batched vs. non-batched route
  • Enabled common verbosity arguments for pytest

:beetle: Bug Fixes

• Fixed incorrect type castings and mismatched operators
• Fixed double division of logistic model regularization by sum of weights
• Fixed incorrect processing of logistic regularization when passed to sklearn
• GPU version of Logistic Regression now returns a correct shape of probabilities

Acknowledgements

Thanks to everyone who helped us make 2025.8.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @razdoburdin, @avolkov-intel, @KateBlueSky, @yuejiaointel, @DDJHB, @kjackiew, @richardnorth3

New Contributors

• @KateBlueSky made their first contribution in https://github.com/uxlfoundation/scikit-learn-intelex/pull/2640

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.7.0...2025.8.0

- Python
Published by maria-Petrova 10 months ago

https://github.com/uxlfoundation/scikit-learn-intelex - Extension for Scikit-learn* 2025.7.0

Extension for Scikit-learn* is happy to introduce 2025.7.0 release!

:rotating_light: What's New

• Introduced new Extension for Scikit-learn* functionality:
  • Added support sklearn 1.6 conformance testing
  • Added grain_size hyperparameter into EmpiricalCovariance and PCA algorithms
  • Enabled model conversion from TreeLite

:beetle: Bug Fixes

• Minor basic stats quality fixes
• Fixed policy changes for spmd
• Fixed Forest dpctl predict queue misalignment
• Fixed logic to check for existence of different oneDAL libraries

Acknowledgements

Thanks to everyone who helped us make 2025.7.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @razdoburdin, @avolkov-intel, @yuejiaointel

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.6.1...2025.7.0

- Python
Published by maria-Petrova 11 months ago

https://github.com/uxlfoundation/scikit-learn-intelex - Extension for Scikit-learn* 2025.6.1

Extension for Scikit-learn* is happy to introduce 2025.6.1 release!

:rotating_light: What's New

• Introduced new Extension for Scikit-learn* functionality:
  • Model builders can now work with XGBoost regression models that involve link functions
  • Improved XGBoost compatibility for object modeling
  • Added a new class with .predict() for logistic regression model builder

:beetle: Bug Fixes

• Bug fixes for decision trees
• Fixed forcing D4P compiler to ICX when building with DPC
• Fixes for sklearn 1.7 pre-release support

Acknowledgements

Thanks to everyone who helped us make 2025.6.1 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @razdoburdin, @avolkov-intel, @yuejiaointel

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.5.0...2025.6.1

- Python
Published by maria-Petrova 12 months ago

https://github.com/uxlfoundation/scikit-learn-intelex - Extension for Scikit-learn* 2025.5.0

Extension for Scikit-learn* is happy to introduce 2025.5.0 release!

:rotating_light: What's New

• Introduced new Extension for Scikit-learn* functionality:
  • Add new parameters for linear regression
  • Accelerated arrayapi inputs for sklearnex's `validatedataandchecksample_weight`
  • Model builders can now work with XGBoost regression models that involve link functions
  • XGBoost model objects don't get invalidated after converting them to daal4py
  • There's now a class with .predict() for logistic regression model builder

:beetle: Bug Fixes

• Normalized Decision Tree .values attribute to match sklearn
• Fixed incorrect scale of base_score being used for XGB regression objectives
• Fixed csr k-Means Init offloading when SYCL CPU device is unavailable

Acknowledgements

Thanks to everyone who helped us make 2025.5.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @razdoburdin, @avolkov-intel, @yuejiaointel

New Contributors

• @tanannie22 made their first contribution in https://github.com/uxlfoundation/scikit-learn-intelex/pull/2343

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.4.0...2025.5.0

- Python
Published by maria-Petrova about 1 year ago

https://github.com/uxlfoundation/scikit-learn-intelex - Extension for Scikit-learn* 2025.4.0

Extension for Scikit-learn* is happy to introduce 2025.4.0 release!

:rotating_light: What's New

• Introduced new Extension for Scikit-learn* functionality:
  • Improved the help documentation by adding clickable links
  • Added support for Python 3.13 support
  • Added support for Sklearn 1.6

Acknowledgements

Thanks to everyone who helped us make 2025.4.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @razdoburdin, @avolkov-intel, @yuejiaointel,

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.2.0...2025.4.0

- Python
Published by maria-Petrova about 1 year ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2025.2.0

Intel® Extension for Scikit-learn* is happy to introduce 2025.2.0 release!

:rotating_light: What's New

• Introduced new Intel® Extension for Scikit-learn* functionality:
  • Enabled linear regression on GPU with non-PSD systems
  • Added serialization for IncrementalBasicStatistics, IncrementalEmpiricalCovariance, IncrementalPCA
  • Moved Ridge Regression out of preview
  • Disabled patching for k-Means(n_clusters=1)
  • Added sklearnex version of validate_data, _check_sample_weight
  • Upgraded IncrementalLinearRegression for underdetermined systems
  • Enabled new RNG engines support

:beetle: Bug Fixes

• Enabled proper GPU offloading with fp64 support when dpctl unavailable
• Fixed to_table for a non-array input when a low-precision queue is used
• Fixed incremental pca example patching logic

Acknowledgements

Thanks to everyone who helped us make 2025.2.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @razdoburdin, @avolkov-intel, @yuejiaointel,

New Contributors

• @yuejiaointel made their first contribution in https://github.com/uxlfoundation/scikit-learn-intelex/pull/2229

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.1.0...2025.2.0

- Python
Published by maria-Petrova over 1 year ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2025.1.0

Intel® Extension for Scikit-learn* is happy to introduce 2025.1.0 release!

:rotating_light: What's New

• Introduced new Intel® Extension for Scikit-learn* functionality:
  • Enabled accelerated Linear Regression for overdetermined systems
  • Enabled hyperparameter support for Random Forest classifier inference
  • Enabled serialization in daal4py algorithm classes

:beetle: Bug Fixes

• Fixed int overflow in FTI model convertor
• Updated BasicStatistics and IncrementalBasicStatistics to follow additional sklearn conventions
• Fixed n_jobs support coverage to indirectly-supported oneDAL methods
• Fixed KMeans score check in _onedal_*_supported and n_jobs support for score
• Corrected skips in design rule checks (test_common.py) caused by fragile whitelist_to_blacklist
• Fixed test_estimators[LogisticRegression()-check_estimators_unfitted] conformance for gpu support
• Updated functional support fallback logic for a DPNP/DPCTL ndarray inputs
• Fixed an issue in aliased _onedal_cpu_supported and _onedal_gpu_supported in fit_check_before_support_check
• Fixed logic of k-NN algos kneighbors() call when algorithm='brute' and fit with GPU

:hammer: Library Engineering

• Added Python 3.13 support for Intel® Extension for Scikit-learn* packages
• Added Sklearn 1.6 support for Intel® Extension for Scikit-learn* packages

Acknowledgements

Thanks to everyone who helped us make 2025.1.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @david-cortes-intel, @icfaust, @napetrov, @maria-Petrova, @homksei, @ahuber21, @ethanglaser, @samir-nasibli, @olegkkruglov, @razdoburdin, @avolkov-intel, @md-shafiul-alam

Full Changelog: https://github.com/uxlfoundation/scikit-learn-intelex/compare/2025.0.0...2025.1.0

- Python
Published by maria-Petrova over 1 year ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2025.0.0

Intel® Extension for Scikit-learn* is happy to introduce 2025.0.0 release!

:rotating_light: What's New

• Introduced new Intel® Extension for Scikit-learn* functionality:
  • Enabled functional support for Array API
  • k-Means algorithm is moved out of preview namespace
  • SHAP value support for XGBoost's binary classification models
  • SPMD interfaces support: IncrementalLinearRegression, IncrementalPCA, IncrementalEmpiricalCovariance

:beetle: Bug Fixes

• Fix issues with sklearn conformance for preview Ridge for 2024.6.0
• Fix on preview ridge tests having too little error tolerance for coefficients assertions
• Fix for Logistic Regression loss scaling
• Fix to prevent support_usm_ndarray from changing queue if explicitly provided
• Fix Multivariate Ridge Regression coefficients
• Fix circular import in daal4py/sklearnex device_offloading
• Align sklearnex BasicStatistics._onedal_fit with other algos

:x: Deprecation Notice

• Removed Python 3.8 support

Acknowledgements

Thanks to everyone who helped us make 2025.0.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @icfaust, @napetrov, @maria-Petrova, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @emmwalsh, @olegkkruglov, @razdoburdin, @avolkov-intel, @md-shafiul-alam, @david-cortes-intel

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.7.0...2025.0.0

- Python
Published by maria-Petrova over 1 year ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.7.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.7.0 release!

:rotating_light: What's New

• Introduced new Intel® Extension for Scikit-learn* functionality:
  • Sparse data support for LogisticRegression
  • Basic Statistic API improvement
  • Added random_state warning to SVM probability estimates
  • Unified daal4py and sklearnex builds

:beetle: Bug Fixes

• Fix issues with sklearn conformance for preview Ridge for 2024.6.0
• Fix on preview ridge tests having too little error tolerance for coefficients assertions
• Fix for Logistic Regression loss scaling
• Fix to prevent support_usm_ndarray from changing queue if explicitly provided
• Fix Multivariate Ridge Regression coefficients
• Fix circular import in daal4py/sklearnex device_offloading
• Align sklearnex BasicStatistics._onedal_fit with other algos

:x: Deprecation Notice

• Removed Python 3.8 support

Acknowledgements

Thanks to everyone who helped us make 2024.7.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @icfaust, @napetrov, @maria-Petrova, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @emmwalsh, @olegkkruglov, @razdoburdin, @avolkov-intel, @md-shafiul-alam

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.6.0...2024.7.0

- Python
Published by maria-Petrova over 1 year ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.6.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.6.0 release!

:rotating_light: What's New

• Introduced new Intel® Extension for Scikit-learn* functionality:
  • Incremental PCA algorithm
  • NumPy 2.0 support
  • scikit-learn 1.5 support
  • CSR data support in Basic Statistics algorithm

:beetle: Bug Fixes

• Fix incorrect numpy to table conversion on Windows
• Fix issues with dpnp/dpctl regressor score method

Acknowledgements

Thanks to everyone who helped us make 2024.6.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @icfaust, @napetrov, @maria-Petrova, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @emmwalsh, @olegkkruglov, @razdoburdin, @avolkov-intel, @md-shafiul-alam

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.5.0...2024.6.0

- Python
Published by maria-Petrova almost 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.5.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.5.0 release!

:rotating_light: What's New

• Introduced new Intel® Extension for Scikit-learn* functionality:
  • IncrementalLinearRegression interface
  • IncrementalEmpiricalCovariance interface to patch_map

:beetle: Bug Fixes

• Fix dpnp/dpctl F-contiguous data processing

Acknowledgements

Thanks to everyone who helped us make 2024.5.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @icfaust, @napetrov, @maria-Petrova, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @emmwalsh, @olegkkruglov, @razdoburdin, @avolkov-intel, @md-shafiul-alam

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.4.0...2024.5.0

- Python
Published by maria-Petrova almost 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.4.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.4.0 release!

:rotating_light: What's New

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

  • IncrementalBasicStatistics interface
  • Added _n_inner_iter attribute for Logistic Regression
  • Added assume_centered capability to EmpiricalCovariance

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

  • PCA

:beetle: Bug Fixes

• Fix sample_weight check for IncrementalBasicStatistics
• Fix dpnp/dpctl slowdown in fit method of neighbors algorithms

Acknowledgements

Thanks to everyone who helped us make 2024.4.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @icfaust, @napetrov, @maria-Petrova, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @emmwalsh, @olegkkruglov, @razdoburdin, @avolkov-intel, @md-shafiul-alam

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.3.0...2024.4.0

- Python
Published by maria-Petrova about 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.3.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.3.0 release!

:rotating_light: What's New

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

  • model_selection in sklearnex namespace
  • SPMD backend library is separated from dpc backend
  • PCA algorithm is moved out of preview namespace

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

  • PCA

:beetle: Bug Fixes

• Fix test_patching routines for intelex-only sklearnex estimators
• Update sklearnex init based on SPMD backend changes
• Fix import error by adding conditional check of OFF_ONEDAL_IFACE flag when importing onedal

:x: Deprecation Notice

• Sklearn estimators in onedal4py LinReg and k-Means algorithms is deprecated for usage

Acknowledgements

Thanks to everyone who helped us make 2024.3.0 release possible!

@Alexsandruss, @Alexandr-Solovev, @Vika-F, @icfaust, @napetrov, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @olegkkruglov, @razdoburdin, @maria-Petrova, @avolkov-intel, @md-shafiul-alam

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.2.0...2024.3.0

- Python
Published by maria-Petrova about 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.2.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.2.0 release!

:rotating_light: What's New

• Introduced new Intel(R) Extension for Scikit-learn* functionality:
  • Incremental Covariance algorithm
  • Logistic Regression algorithm is moved out of preview namespace
  • SPMD interfaces support: Logistic Regression, Covariance

:hammer: Library Engineering

• Enabled scikit-learn 1.4 support

:beetle: Bug Fixes

• Adjusted n_jobs parameter setting
• Updated DPCPP detection in setup
• Fix of k-Means SPMD timeout
• Correct disabling of CatBoost SHAP
• Fix LocalOutlierFactor kneighbors method

Acknowledgements

Thanks to everyone who helped us make 2024.2.0 release possible!

@Alexsandruss, @icfaust, @napetrov, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @olegkkruglov, @razdoburdin, @maria-Petrova, @avolkov-intel

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.1.0...2024.2.0

- Python
Published by maria-Petrova about 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.1.0

Intel® Extension for Scikit-learn* is happy to introduce 2024.1.0 release!

:rotating_light: What's New

• New Intel® Extension for Scikit-learn* functionality:
  • SHAP support for symmetric CatBoost models
  • Added oneDAL LinReg and Covariance hyperparameters API
  • Added LogisticRegression interface to the preview section
  • Initial support of n_jobs parameter

Acknowledgements

Thanks to everyone who helped us make 2024.1.0 release possible!

@Alexsandruss, @icfaust, @napetrov, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @olegkkruglov, @razdoburdin, @KulikovNikita, @maria-Petrova, @avolkov-intel

Full Changelog: https://github.com/intel/scikit-learn-intelex/compare/2024.0.1...2024.1.0

- Python
Published by maria-Petrova over 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn* 2024.0.1

Intel® Extension for Scikit-learn* is happy to introduce 2024.0.1 release!

:rotating_light: What's New

• New Intel(R) Extension for Scikit-learn* functionality:
  • Linear Regression and ensemble algorithms are moved out of preview namespace
• New Model Builders functionality:
  • SHAP calculation is added to GBT regression

:hammer: Library Engineering

• Added Python 3.12 support for daal4py and Intel(R) Extension for Scikit-learn* packages

:books: Support Materials

Faster XGBoost*, LightGBM, and CatBoost Inference on the CPU PS-S3-Ep23-with-scikit-learn-intelex pss3e23 fusion_model with scikit-learn-intelex PS S3E25: Faster regression tuning with sklearnex

:twistedrightwardsarrows: Adoption

TPOT2 AutoML integration

Acknowledgements

Thanks to everyone who helped us make 2024.0.1 release possible!

@Alexsandruss, @icfaust, @napetrov, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @olegkkruglov, @razdoburdin, @KulikovNikita, @maria-Petrova, @avolkov-intel

- Python
Published by maria-Petrova over 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel(R) Extension for Scikit-learn* 2024.0.0

Intel(R) Extension for Scikit-learn* is happy to introduce 2024.0 release!

What's New

• New functionality:
  • DBSCAN and SPMD DBSCAN algorithms

Acknowledgements

Thanks to everyone who helped us make 2024.0 release possible!

@Alexsandruss, @icfaust, @napetrov, @ahuber21, @ethanglaser, @samir-nasibli, @aepanchi, @olegkkruglov, @razdoburdin, @KulikovNikita, @maria-Petrova, @avolkov-intel

- Python
Published by maria-Petrova over 2 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn 2023.2.1

The release of Intel® Extension for Scikit-learn 2023.2.1 introduces the following changes:

🚨 What's New

• sklearn 1.3 support fixes (1, 2, 3)
• Model builders API update

- Python
Published by maria-Petrova almost 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® Extension for Scikit-learn 2023.2.0

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

:x: 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

- Python
Published by maria-Petrova almost 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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 https://github.com/intel/scikit-learn-intelex/commit/4abff0df77475a7e7c3f3da135fdc9dc586f8f1e
• SPMD interface for Random Forest, kNN and PCA https://github.com/intel/scikit-learn-intelex/commit/e887d996a3da57ba75a1cbdaf26d3fea03ad882f, https://github.com/intel/scikit-learn-intelex/commit/97f0d470c5794cd63da18524f65053b89ab4177d, https://github.com/intel/scikit-learn-intelex/commit/2405630020da1563646aef768f12951c6f0d2dc3
• Native support for DPCTL https://github.com/intel/scikit-learn-intelex/commit/ca284998ffb98cb318a8ee6b6a9eb31a68b4d509
• Patching for sklearn's LocalOutlierFactor https://github.com/intel/scikit-learn-intelex/commit/7ae01712a75dbda9bf21c9ed444722ff7ac03dd2

- Python
Published by maria-Petrova about 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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
  • sklearnispatched() 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.

- Python
Published by maria-Petrova about 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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 https://github.com/intel/scikit-learn-intelex/commit/5275ebac37c416ce110634c6cee7b56f872ed71b
• Fixes for balanced classes and number of iterations in SVM https://github.com/intel/scikit-learn-intelex/commit/14849ee7190f5701e4eab2ad923fce9125e904ff, https://github.com/intel/scikit-learn-intelex/commit/4872a8ea0afa22813f1a4446ef5fc0d608660283, https://github.com/intel/scikit-learn-intelex/commit/9d0a05b80c09aa3930107e2eca233cd7b872593c
• Fix for gamma parameter in KMeans https://github.com/intel/scikit-learn-intelex/commit/1dca20c3761197d082d548f27f698d6389e60fbe

- Python
Published by maria-Petrova about 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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.

- Python
Published by maria-Petrova about 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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

- Python
Published by Alexsandruss over 3 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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

- Python
Published by Alexsandruss almost 4 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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

- Python
Published by Pahandrovich over 4 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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 patchsklearn patchsklearn(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.

- Python
Published by KalyanovD over 4 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 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 dpcppcpprt 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: python import sys import os import site sys.path.append(os.path.join(os.path.dirname(site.getsitepackages()[0]), "site-packages"))

- Python
Published by PetrovKP almost 5 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel(R) Extension for Scikit-learn 2021.2.3

🚨 New Features

• Added support of patching scikit-learn version 1.0. scikit-learn version 0.21. * is no longer supported

- Python
Published by PetrovKP about 5 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel(R) Extension for Scikit-learn 2021.2

⚡️ 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

• Medium blogs:
  • Intel Gives Scikit-Learn the Performance Boost Data Scientists Need
  • From Hours to Minutes: 600x Faster SVM
  • Improve the Performance of XGBoost and LightGBM Inference
  • Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit
• Kaggle kernels:
  • Accelerate sklearn algorithms using sklearnex
  • Fast SVC using scikit-learn-intelex for NLP
  • Fast SVC using scikit-learn-intelex for MNIST
  • Fast KNN using  scikit-learn-intelex for MNIST
  • Using scikit-learn-intelex for What's Cooking

🛠️ 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

- Python
Published by PetrovKP about 5 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® daal4py 2020 Update 3 Patch 1

What's New

• Added support of patching scikit-learn version 0.24.

- Python
Published by PetrovKP over 5 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® daal4py 2021.1

What's New

Introduced new daal4py functionality:

• GPU:
  • Batch algorithms: K-means, Covariance, PCA, Logistic Regression, Linear Regression, Random Forest Classification and Regression, Gradient Boosting Classification and Regression, kNN, SVM, DBSCAN and Low-order moments
  • Online algorithms: Covariance, PCA, Linear Regression and Low-order moments

Improved daal4py performance for the following algorithms:

• CPU:
  • Logistic Regression training and prediction
  • k-Nearest Neighbors prediction with Brute Force method
  • Logistic Loss and Cross Entropy objective functions

Introduced new functionality for scikit-learn patching through daal4py:

• CPU:
  • Acceleration of NearestNeighbors and KNeighborsRegressor scikit-learn estimators with Brute Force and K-D tree methods
  • Acceleration of TSNE scikit-learn estimator
• GPU:
  • Intel GPU support in scikit-learn for DBSCAN, K-means, Linear and Logistic Regression

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

• CPU:
  • LogisticRegression fit, predict and predict_proba methods
  • KNeighborsClassifier predict, predict_proba and kneighbors methods with “brute” method

Known Issues

• train_test_split in daal4py patches for Scikit-learn can produce incorrect shuffling on Windows*

Installation

To install this package with conda run the following: bash conda install -c intel daal4py

- Python
Published by KalyanovD over 5 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - Intel® daal4py 2020 Update 3

What's New in Intel® daal4py 2020 Update 3:

Introduced new daal4py functionality:

• Conversion of trained XGBoost* and LightGBM* models into a daal4py Gradient Boosted Trees model for fast prediction
• Support of Modin* DataFrame as an input
• Brute Force method for k-Nearest Neighbors classification algorithm, which for datasets with more than 13 features demonstrates a better performance than the existing K-D tree method
• k-Nearest Neighbors search for K-D tree and Brute Force methods with computation of distances to nearest neighbors and their indices

Extended existing daal4py functionality:

• Voting methods for prediction in k-Nearest Neighbors classification and search: based on inverse-distance and uniform weighting
• New parameters in Decision Forest classification and regression: minObservationsInSplitNode, minWeightFractionInLeafNode, minImpurityDecreaseInSplitNode, maxLeafNodes with best-first strategy and sample weights
• Support of Support Vector Machine (SVM) decision function for Multi-class Classifier

Improved daal4py performance for the following algorithms:

• SVM training and prediction
• Decision Forest classification training
• RBF and Linear kernel functions

Introduced new functionality for scikit-learn patching through daal4py:

• Acceleration of KNeighborsClassifier scikit-learn estimator with Brute Force and K-D tree methods
• Acceleration of RandomForestClassifier and RandomForestRegressor scikit-learn estimators
• Sparse input support for KMeans and Support Vector Classification (SVC) scikit-learn estimators
• Prediction of probabilities for SVC scikit-learn estimator
• Support of ‘normalize’ parameter for Lasso and ElasticNet scikit-learn estimators

Improved performance of the following functionality for scikit-learn patching through daal4py:

• train_test_split()
• Support Vector Classification (SVC) fit and prediction

To install this package with conda run the following: conda install -c intel daal4py

- Python
Published by PetrovKP over 5 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - daal4py 2020.2

Introduced new functionality: * Thunder method for Support Vector Machine (SVM) training algorithm, which demonstrates better training time than the existing sequential minimal optimization method

Extended existing functionality: * Training with the number of features greater than the number of observations for Linear Regression, Ridge Regression, and Principal Component Analysis * New sample_weights parameter for SVM algorithm * New parameter in K-Means algorithm, resultsToEvaluate, which controls computation of centroids, assignments, and exact objective function

Improved performance for the following: * Support Vector Machine training and prediction, Elastic Net and LASSO training, Principal Component Analysis training and transform, K-D tree based k-Nearest Neighbors prediction * K-Means algorithm in batch computation mode * RBF kernel function

Deprecated 32-bit support: * 2020 product line will be the last one to support 32-bit

Introduced improvements to daal4py library: * Performance optimizations for pandas input format * Scikit-learn compatible API for AdaBoost classifier, Decision Tree classifier, and Gradient Boosted Trees classifier and regressor

Improved performance of the following Intel Scikit-learn algorithms and functions: * fit and prediction in K-Means and Support Vector Classification (SVC), fit in Elastic Net and LASSO, fit and transform in PCA * Support Vector Classification (SVC) with non-default weights of samples and classes * traintestsplit() and assertallfinite()

To install this package with conda run the following: conda install -c intel daal4py

- Python
Published by PivovarA almost 6 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - daal4py 2020.1

Introduced new functionality: * Elastic Net algorithm with L1 and L2 regularization in batch computation mode. The algorithm supports various optimization solvers that handle non-smooth functions. * Probabilistic classification for Decision Forest Classification algorithm with a choice voting method to calculate probabilities.

Extended existing functionality: * Performance optimizations for distributed Spark samples, K-means algorithm for some input dimensions, Gradient Boosted Trees training stage for large datasets on multi-core platforms and Decision Forest prediction stage for datasets with a small number of observations on processors that support Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512) * Performance optimizations across algorithms that use SOA (Structure Of Arrays) NumericTable as an input on processors that support Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

- Python
Published by PivovarA almost 6 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - daal4py 2020.0

Added support for Brownboost, Logistboost as well as Stump regression and Stump classification algorithms to daal4py. Added support for Adaboost classification algorithm, including support for method="SAMME" or "SAMMER" for multi-class data. "Variable Importance" feature has been added in Gradient Boosting Trees. Ability to compute class prediction probabilities has been added to appropriate classifiers, including logistic regression, tree-based classifiers, etc.

- Python
Published by Alexander-Makaryev over 6 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - 2019.5

Single node support for DBSCAN, LASSO, Coordinate Descent (CD) solver algorithms Distributed model support for SVD, QR, K-means init++ and parallel++ algorithms

- Python
Published by Alexander-Makaryev over 6 years ago

https://github.com/uxlfoundation/scikit-learn-intelex - daal4py 2019.3

Product release with Intel(R) Parallel Studio 2019 Update 3

- Python
Published by fschlimb about 7 years ago