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