Intel® oneAPI Data Analytics Library 2021.2

The release introduces the following changes:

Library Engineering:

• Enabled new PyPI distribution channel for daal4py:
  • Four latest Python versions (3.6, 3.7, 3.8, 3.9) 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
• Introduced CMake support for oneDAL examples

Support Materials

The following additional materials were created:

• 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 scikit-learn algorithms using daal4py
  • Fast SVC using daal4py for NLP
  • Fast SVC using daal4py for MNIST
  • Fast KNN using daal4py for MNIST
  • [daal4py] TF-IDF with OvR SVM: What's Cooking

What's New

Introduced new oneDAL and daal4py functionality:

• CPU:
  • Hist method for Decision Forest Classification and Regression, which outperforms the existing exact method
  • Bit-to-bit results reproducibility for: Linear and Ridge regressions, LASSO and ElasticNet, KMeans training and initialization, PCA, SVM, kNN Brute Force method, Decision Forest Classification and Regression
• GPU:
  • Multi-node multi-GPU algorithms: KMeans (batch), Covariance (batch and online), Low order moments (batch and online) and PCA
  • Sparsity support for SVM algorithm

Improved oneDAL and daal4py performance for the following algorithms:

• CPU:
  • Decision Forest training Classification and Regression
  • Support Vector Machines training and prediction
  • Logistic Regression, Logistic Loss and Cross Entropy for non-homogeneous input types
• GPU:
  • Decision Forest training Classification and Regression
  • All algorithms with GPU kernels (as a result of migration to Unified Shared Memory data management)
  • Reduced performance overhead for oneAPI C++ interfaces on CPU and oneAPI DPC++ interfaces on GPU

Added technical preview features in Graph Analytics:

• CPU:
  • Local and Global Triangle Counting

Introduced new functionality for scikit-learn patching through daal4py:

• CPU:
  • Patches for four latest scikit-learn releases: 0.21.X, 0.22.X, 0.23.X and 0.24.X
  • Acceleration of roc_auc_score function
  • Bit-to-bit results reproducibility for: LinearRegression, Ridge, SVC, KMeans, PCA, Lasso, ElasticNet, tSNE, KNeighborsClassifier, KNeighborsRegressor, NearestNeighbors, RandomForestClassifier, RandomForestRegressor

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

• CPU
  • RandomForestClassifier and RandomForestRegressor scikit-learn estimators: training and prediction
  • Principal Component Analysis (PCA) scikit-learn estimator: training
  • Support Vector Classification (SVC) scikit-learn estimators: training and prediction
  • Support Vector Classification (SVC) scikit-learn estimator with the probability==True parameter: training and prediction

Fixed the following issues:

• Scikit-learn patching:

  • Improved accuracy of RandomForestClassifier and RandomForestRegressor scikit-learn estimators
  • Fixed patching issues with pairwise_distances
  • Fixed the behavior of the patch_sklearn and unpatch_sklearn functions
  • Fixed unexpected behavior that made accelerated functionality unavailable through scikit-learn patching if the unput was not of float32 or float64 data types. Scikit-learn patching now works with all numpy data types.
  • Fixed a memory leak that appeared when DataFrame from pandas was used as an input type
  • Fixed performance issue for interoperability with Modin

• daal4py:

  • Fixed the crash of SVM and kNN algorithms on Windows on GPU

• oneDAL:

  • Improved accuracy of Decision Forest Classification and Regression on CPU
  • Improved accuracy of KMeans algorithm on GPU
  • Improved stability of Linear Regression and Logistic Regression algorithms on GPU

​​Known Issues

• oneDAL vars.sh script does not support kornShell

Intel® oneAPI Data Analytics Library 2021.1

The release contains all functionality of Intel® DAAL. See Intel® DAAL release notes for more details.

What's New

Library Engineering:

• Renamed the library from Intel® Data Analytics Acceleration Library to Intel® oneAPI Data Analytics Library and changed the package names to reflect this.
• Deprecated 32-bit version of the library.
• Introduced Intel GPU support for both OpenCL and Level Zero backends.
• Introduced Unified Shared Memory (USM) support

Introduced new Intel® oneDAL and 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
  • Added Data Management functionality to support DPC++ APIs: a new table type for representation of SYCL-based numeric tables (SyclNumericTable) and an optimized CSV data source

Improved Intel® oneDAL and 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

Added Technical Preview Features in Graph Analytics:

• CPU:
  • Undirected graph without edge and vertex weights (undirected_adjacency_array_graph), where vertex indices can only be of type int32
  • Jaccard Similarity Coefficients for all pairs of vertices, a batch algorithm that processes the graph by blocks

Aligned the library with Intel® oneDAL Specification 1.0 for the following algorithms:

• CPU/GPU:
  • K-means, PCA, kNN

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

• Intel® oneDAL DPC++ APIs does not work on GEN12 graphics with OpenCL backend. Use Level Zero backend for such cases.
• train_test_split in daal4py patches for Scikit-learn can produce incorrect shuffling on Windows*

Intel® DAAL 2020 Update 3

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

Introduced new Intel® DAAL and daal4py functionality:

• 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 Intel® DAAL and 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 Intel® DAAL and daal4py performance for the following algorithms:

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

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

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

Dependencies

fix one-algorithm build and spicific prediction case after probabilit…

DAAL 2020

Update version of bzip2 1.0.4 to 1.0.8 in mkl-fpk (mklfpk_lnx_20180112_10 / mklfpk_mac_20180112_10 / mklfpk_win_20180112_10)

DAAL 2019 Update 4

Revision: 33235

Linux* (32-bit and 64-bit binary): l_daal_oss_p_2019.4.007.tgz
macOS* (32-bit and 64-bit binary): m_daal_oss_p_2019.4.007.tgz

Note: Please, use Git client with enabled Git LFS module to clone repository if you want to get sources. We are working with GitHub support to enable correct work of archives ”Source code (zip)" and "Source code (tar.gz)".