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oneAPI Data Analytics Library (oneDAL) is a powerful machine learning library that helps you accelerate big data analysis at all stages: preprocessing, transformation, analysis, modeling, validation, and decision making.
The library implements classical machine learning algorithms. The boost in their performance is achieved by leveraging the capabilities of Intel® hardware.
The oneDAL is part of the UXL Foundation and is an implementation of the oneAPI specification for oneDAL component.
There are different ways for you to build high-performance data science applications that use the advantages of oneDAL:
- Use oneDAL C++ interfaces with or without SYCL support (learn more)
- Use Intel(R) Extension for Scikit-learn* to accelerate existing scikit-learn code without changing it
- Use daal4py, a standalone package with Python API for oneDAL Deprecation Notice: The Java interfaces are removed from the oneDAL library.
Check the System Requirements before installing to ensure compatibility with your system.
There are several options available for installing oneDAL:
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Binary Distribution: You can download pre-built binary packages from the following sources:
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Intel® oneAPI:
- Download as Part of the Intel® oneAPI Base Toolkit
- Download as the Stand-Alone Intel® oneAPI Data Analytics Library
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Anaconda:
Channel Version intel conda-forge
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Source Distribution: You can build the library from source. To do this, download the specific version of oneDAL from the official GitHub repository and follow the instructions in the INSTALL.md.
C++ Examples:
Python Examples:
oneDAL documentation:
Other related documentation:
oneDAL library is used for Spark MLlib acceleration as part of OAP MLlib project and allows you to get a 3-18x increase in performance compared to the default Apache Spark MLlib.
Technical details: FPType: double; HW: 7 x m5.2xlarge AWS instances; SW: Intel DAAL 2020 Gold, Apache Spark 2.4.4, emr-5.27.0; Spark config num executors 12, executor cores 8, executor memory 19GB, task cpus 8
oneDAL supports distributed computation mode that shows excellent results for strong and weak scaling:
| oneDAL K-Means fit, strong scaling result | oneDAL K-Means fit, weak scaling results |
|---|---|
 |
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Technical details: FPType: float32; HW: Intel Xeon Processor E5-2698 v3 @2.3GHz, 2 sockets, 16 cores per socket; SW: Intel® DAAL (2019.3), MPI4Py (3.0.0), Intel® Distribution Of Python (IDP) 3.6.8; Details available in the article https://arxiv.org/abs/1909.11822
The oneDAL project is governed by the UXL Foundation and you can get involved in this project in multiple ways. It is possible to join the AI Special Interest Group (SIG) meetings where the group discuss and demonstrates work using this project. Members can also join the Open Source and Specification Working Group meetings.
You can also join the mailing lists for the UXL Foundation to be informed of when meetings are happening and receive the latest information and discussions.
You can contribute to this project and also contribute to the specification for this project, read the CONTRIBUTING page for more information.
Ask questions and engage in discussions with oneDAL developers, contributers, and other users through the following channels:
You may reach out to project maintainers privately at onedal.maintainers@intel.com.
To report a vulnerability, refer to Intel vulnerability reporting policy.
We welcome community contributions. Check our contributing guidelines to learn more. You can also contact the oneDAL team via UXL Foundation Slack using #onedal channel.
oneDAL is distributed under the Apache License 2.0 license. See LICENSE for more information.
oneMKL FPK microlibs are distributed under Intel Simplified Software License. Refer to third-party-programs-mkl.txt for details.