Intel® oneAPI Data Analytics Library 2023.2.0

The release Intel® oneAPI Data Analytics Library 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® oneAPI Data Analytics Library 2023.1.1

The release Intel® oneAPI Data Analytics Library 2023.1.1 introduces the following changes:

🚨 What's New

• Introduced new oneDAL functionality:
  • oneAPI interfaces for Logistic Loss: 98fc59c
  • Random splitting strategy in Decision Forest algorithm: 5e080cf
• Performance fix for features sampling in Decision Forest algorithm: af225ad

Intel® oneAPI Data Analytics Library 2023.1.0

The release Intel® oneAPI Data Analytics Library 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%
• Enabled NuGet distribution channel for Intel® oneDAL on Linux and MacOS

🚨 What's New

• Introduced new Intel® oneDAL functionality:
  • Distributed Linear Regression, kNN, PCA algorithms
• 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® oneAPI Data Analytics Library 2023.0.1

The release Intel® oneAPI Data Analytics Library 2023.0.1 introduces the following changes:

🚨 What's New

• Performance improvements for tSNE algorithm: b8fb329
• Fixes in DBSCAN algorithm: f3cbe18
• Fix for dynamic library versions on Linux and macOS: 10caf82

Intel® oneAPI Data Analytics Library 2023.0.0

The release Intel® oneAPI Data Analytics Library 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® oneAPI Data Analytics Library 2021.7.1

The release Intel® oneAPI Data Analytics Library 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

• zlib and bzip2 methods of compression were deprecated. They are dispatched to the lzo method starting this version
• Optional results (eigenvectors, eigenvalues, variances and means) and precomputed method for PCA algorithm.

Intel® oneAPI Data Analytics Library 2021.6

The release Intel® oneAPI Data Analytics Library 2021.6 introduces the following changes:

📚 Support Materials:

Kaggle kernels for Intel® Extension for Scikit-learn:

• 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 of oneDAL algorithms:
  • Optimized data conversion for tables with column-major layout in host memory to tables with row-major layout in device memory
  • Optimized the computation of Minkowski distances in brute-force kNN on CPU
  • Optimized Covariance algorithm
  • Added DPC++ column-wise atomic reduction
• Introduced new oneDAL functionality:
  • KMeans distributed random dense initialization
  • Distributed PcaCov
  • sendrecv_replace communicator method
• Added new parameters to oneDAL algorithms:
  • Weights in Decision Forest for CPU
  • Cosine and Chebyshev distances for KNN on GPU

Intel® oneAPI Data Analytics Library 2021.5

The release introduces the following changes:

📚 Support Materials

The following additional materials were created:

• oneDAL samples:

  • oneCCL-based distributed K-means, Covariance and DBSCAN samples
  • MPI samples for Basic Statistics and Decision Forest

• Intel® Extension for Scikit-learn samples:

  • 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
  • Demo samples of the Modin usage

• daal4py samples:

  • An example of Catboost converter usage

• Kaggle kernels for Intel® Extension for Scikit-learn:

  • [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

🛠️ Library Engineering

• Reduced the size of oneDAL library by approximately ~15%.

🚨 What's New

• Introduced new oneDAL functionality:
  • Distributed algorithms for Covariance, DBSCAN, Decision Forest, Low Order Moments
  • oneAPI interfaces for Linear Regression, DBSCAN, KNN
• Improved error handling for distributed algorithms in oneDAL in case of compute nodes failures
• Improved performance for the following oneDAL algorithms:
  • Louvain algorithm
  • KNN and SVM algorithms on GPU
• Introduced new functionality for Intel® Extension for Scikit-learn:
  • Scikit-learn 1.0 support
• Fixed the following issues:
  • Stabilized the results of Linear Regression in oneDAL and Intel® Extension for Scikit-learn
  • Fixed an issue with RPATH on MacOS

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.

Intel® oneAPI Data Analytics Library 2021.3

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"))