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…

…y feature (#177)

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

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