This article includes the Release Notes for Intel® oneAPI Data Analytics Library (oneDAL)
||2021.1 Release Update
oneDAL is the library of Intel® architecture optimized building blocks covering all stages of compute-intense data analytics: data acquisition from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making.
Please see dedicate system requirements article.
The release introduces the following changes:
The release contains all functionality of Intel® DAAL. See Intel® DAAL release notes for more details.
- Renamed the library from Intel® Data Analytics Acceleration Library to 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® DAAL and daal4py functionality:
- 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 oneDAL and daal4py performance for the following algorithms:
- 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:
- 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
- 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*
- The following daal4py examples do not work on Intel® Iris Xe MAX with float64 compute mode:
Run daal4py examples using float32 compute mode instead:
- Use np.float32 data type for input data. To do this, add parameter t=np.float32 to the readcsv function used in the examples.
- Set the parameter fptype to float in the algorithm object constructor: fptype='float'.
- Switch on float64 software emulation on Intel® Iris Xe MAX
- K-Means example in daal4py (examples/sycl/kmeans_batch.py) produces different results on GPU and CPU. To avoid failures, comment assert statements that compare GPU results and classic results in the example.
- DBSCAN example in daal4py (examples/sycl/dbscan_batch.py) hangs when it is running on CPU with data wrapped in sycl_buffer. To avoid hangs, do not pass sycl_buffer objects to DBSCAN on CPU.
Getting Started Guide
Please refer to oneDAL Getting Started Guide
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