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Intel(R) oneAPI Data Analytics Library (oneDAL) is a library that helps speed up big data analysis. We provide highly optimized algorithmic building blocks for all stages of data analytics: preprocessing, transformation, analysis, modeling, validation, and decision making. Our algorithms suppost batch, online, and distributed processing modes of computation.
The current version of oneDAL provides Data Parallel C++ (DPC++) API extensions to the traditional C++ interface.
Technical preview features are introduced to gain early feedback from developers. A preview feature is subject to change in the future releases. Using a preview feature in a production code base is therefore strongly discouraged.
The only preview feature at the moment is MultiNodeBatch for K-Means, a stepless distributed algorithm based on oneCCL.
Intel(R) oneAPI Data Analytics Library is an extenstion of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL).
This repository contains branches corresponding to both oneAPI and classical versions of the library. We encourage you to use oneDAL located under the master branch.
| Product | Latest release | Branch | Resources |
|---|---|---|---|
| oneDAL | 2021.1-beta06 | master rls/onedal-beta06-rls |
Home page Documentation System Requirements |
| Intel(R) DAAL | 2020 Gold | rls/daal-2020-rls rls/daal-2020-mnt (contains ongoing fixes) |
Home page Developer Guide System Requirements |
You can install oneDAL:
- from oneDAL home page as a part of Intel(R) oneAPI Base Toolkit.
- from GitHub*.
See Installation from Sources for details.
Examples for different programming languages:
Data Examples for different computation modes:
Intel(R) DAAL provides downloadable API References for C++, Python, and Java.
You can also use daal4py, a simplified Python API to Intel(R) DAAL that allows fast usage of the framework suited for Data Scientists or Machine Learning users.