Intel(R) oneAPI Data Analytics Library

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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.

Table of Contents

• Technical Preview Features
• oneDAL and Intel(R) DAAL
• Installation
• Examples
• Documentation
• API

Technical Preview Features

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 preview features list:

• MultiNodeBatch for K-Means, a stepless distributed algorithm based on oneCCL
• Graph Analytics:
  • Undirected graph without edge and vertex weights (undirected_adjacency_array_graph) - 32bit vertex index only
  • Jaccard Similarity Coefficients for all vertex pairs, a batch algorithm which processes the graph by blocks

oneDAL and Intel(R) DAAL

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.

Installation

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

Examples for different programming languages:

• C++
• C++ SYCL*
• Java

Data Examples for different computation modes:

• Batch
• Distributed
• Online

Documentation

• Get Started
• System Requirements
• oneDAL documentation
• Specifications
• Release Notes
• Known Issues

API

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.