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https://github.com/oneapi-src/onedal
oneAPI Data Analytics Library (oneDAL)
https://github.com/oneapi-src/onedal
ai-inference ai-machine-learning ai-training analytics big-data cpp data-analysis data-science hacktoberfest machine-learning machine-learning-algorithms oneapi onedal swrepo
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oneAPI Data Analytics Library (oneDAL)
- Host: GitHub
- URL: https://github.com/oneapi-src/onedal
- Owner: oneapi-src
- License: apache-2.0
- Created: 2016-03-28T22:39:32.000Z (over 8 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T10:51:42.000Z (19 days ago)
- Last Synced: 2024-10-29T10:57:00.001Z (19 days ago)
- Topics: ai-inference, ai-machine-learning, ai-training, analytics, big-data, cpp, data-analysis, data-science, hacktoberfest, machine-learning, machine-learning-algorithms, oneapi, onedal, swrepo
- Language: C++
- Homepage: https://software.intel.com/en-us/oneapi/onedal
- Size: 84.8 MB
- Stars: 611
- Watchers: 48
- Forks: 214
- Open Issues: 54
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
README
# oneAPI Data Analytics Library 
[Installation](#installation) | [Documentation](#documentation) | [Support](#support) | [Examples](#examples) | [How to Contribute](CONTRIBUTING.md)
[](https://dev.azure.com/daal/DAAL/_build/latest?definitionId=5&branchName=main)
[](https://github.com/oneapi-src/oneDAL/blob/main/LICENSE)
[](https://www.bestpractices.dev/projects/8859)
[](https://securityscorecards.dev/viewer/?uri=github.com/oneapi-src/oneDAL)
[](https://github.com/oneapi-src/oneDAL/discussions)
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](http://www.uxlfoundation.org) and is an implementation of the [oneAPI specification](https://spec.oneapi.io) for oneDAL component.
## Usage
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](https://oneapi-src.github.io/oneDAL/#oneapi-vs-daal-interfaces))
- Use [Intel(R) Extension for Scikit-learn*](https://intel.github.io/scikit-learn-intelex/) to accelerate existing scikit-learn code without changing it
- Use [daal4py](https://github.com/intel/scikit-learn-intelex/tree/main/daal4py), a standalone package with Python API for oneDAL
Deprecation Notice: The Java interfaces are removed from the oneDAL library.
## Installation
Check the [System Requirements](https://oneapi-src.github.io/oneDAL/system-requirements.html) before installing to ensure compatibility with your system.
There are several options available for installing oneDAL:
- **Binary Distribution**: You can download pre-built binary packages from the following sources:
- Intel® oneAPI:
- Download as Part of the [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onedal.html#gs.8xrue2)
- Download as the Stand-Alone [Intel® oneAPI Data Analytics Library](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onedal.html#gs.8xrue2)
- Anaconda:
| Channel | Version |
|:-------:|:-------:|
| intel | [](https://anaconda.org/intel/dal-devel) |
| conda-forge | [](https://anaconda.org/conda-forge/dal-devel) |
- [NuGet](https://www.nuget.org/packages/inteldal.devel.linux-x64)
- **Source Distribution**: You can build the library from source. To do this, [download the specific version of oneDAL](https://github.com/oneapi-src/oneDAL/releases) from the official GitHub repository and follow the instructions in the [INSTALL.md](INSTALL.md).
## Examples
C++ Examples:
- [oneAPI interfaces with SYCL support](https://github.com/oneapi-src/oneDAL/tree/main/examples/oneapi/dpc)
- [oneAPI interfaces without SYCL support](https://github.com/oneapi-src/oneDAL/tree/main/examples/oneapi/cpp)
- [DAAL interfaces](https://github.com/oneapi-src/oneDAL/tree/main/examples/daal/cpp)
Python Examples:
- [scikit-learn-intelex](https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks)
- [daal4py](https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py)
Other Examples
- [MPI](https://github.com/oneapi-src/oneDAL/tree/main/samples/daal/cpp/mpi)
- [MySQL](https://github.com/oneapi-src/oneDAL/tree/main/samples/daal/cpp/mysql)
## Documentation
oneDAL documentation:
- [Release Notes](https://github.com/oneapi-src/oneDAL/releases)
- [Get Started Guide](https://oneapi-src.github.io/oneDAL/quick-start.html)
- [Developer Guide and Reference](http://oneapi-src.github.io/oneDAL/)
Other related documentation:
- [daal4py documentation](https://intelpython.github.io/daal4py/)
- [Intel(R) Extension for Scikit-learn* documentation](https://intel.github.io/scikit-learn-intelex/)
- [oneDAL Specifications](https://spec.oneapi.com/versions/latest/elements/oneDAL/source/index.html)
## Apache Spark MLlib
oneDAL library is used for Spark MLlib acceleration as part of [OAP MLlib](https://github.com/oap-project/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*
## Scaling
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
:-------------------------:|:-------------------------:
 | 
>*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*
## Governance
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)](https://github.com/uxlfoundation/foundation/tree/main/ai) 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](https://lists.uxlfoundation.org/g/main/subgroups) 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](CONTRIBUTING.md) page for more information.
## Support
Ask questions and engage in discussions with oneDAL developers, contributers, and other users through the following channels:
- [GitHub Discussions](https://github.com/oneapi-src/oneDAL/discussions)
- [Community Forum](https://community.intel.com/t5/Intel-oneAPI-Data-Analytics/bd-p/oneapi-data-analytics-library)
You may reach out to project maintainers privately at [email protected].
### Security
To report a vulnerability, refer to [Intel vulnerability reporting policy](https://www.intel.com/content/www/us/en/security-center/default.html).
### Contribute
We welcome community contributions. Check our [contributing guidelines](CONTRIBUTING.md) to learn more. You can also contact the oneDAL team via [UXL Foundation Slack] using
[#onedal] channel.
[UXL Foundation Slack]: https://slack-invite.uxlfoundation.org/
[#onedal]: https://uxlfoundation.slack.com/channels/onedal
## License
oneDAL is distributed under the Apache License 2.0 license. See [LICENSE](LICENSE) for more information.
[oneMKL FPK microlibs](https://github.com/oneapi-src/oneDAL/releases/tag/Dependencies)
are distributed under Intel Simplified Software License.
Refer to [third-party-programs-mkl.txt](third-party-programs-mkl.txt) for details.