daal
Intel® oneAPI Data Analytics Library
Decision gist · record as of 2026-08-14
Yes, with conditions. oneDAL is actively maintained, production-stable, and offers genuine performance benefits for tabular machine learning on CPUs and GPUs. However, the unclear Intel Simplified Software License requires verification before commercial use, and installation is limited to x86_64 Windows or manylinux_2_28 Linux. Most users will benefit more from the Extension for Scikit-learn (which uses oneDAL transparently) than from direct use of this package.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires tbb runtime dependency; limited to x86_64 Windows or manylinux_2_28 Linux platforms.
- Medium install friction due to platform-specific wheels (manylinux_2_28_x86_64 and win_amd64) and a compiled runtime dependency on tbb.
- Active maintenance with recent releases and an engaged repository (651 stars, last commit 2026-08-14).
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License with unclear license treatment. Not an SPDX-recognized open-source license, so commercial or proprietary use may require explicit clarification with Intel before deployment.
last release 2026-06-10 (65 days) · last repo commit 2026-08-14 · 651 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 170,425 downloads/mo, #10,396 on PyPI
Alternatives
Verify before relying
pip install daal
import daal
# Access oneDAL routines via the daal module interface- Exact Python version support range (requires_python unspecified in metadata)
- Whether the PyPI daal package is the primary Python distribution or a wrapper around the C++ library
- Performance gains relative to standard scikit-learn on typical hardware
- Compatibility matrix with specific GPU vendors and SYCL implementations
What it is and what it does
oneDAL is Intel's oneAPI Data Analytics Library packaged for Python, providing optimized implementations of common machine learning algorithms (regression, clustering, forests) that run on CPUs, GPUs, and distributed clusters. It accelerates computation by leveraging SIMD instructions and cache optimization on CPUs, and SYCL/oneMKL on GPUs. The library is part of the UXL Foundation and is typically used either directly via its C++ interfaces or indirectly through the Extension for Scikit-learn, which patches scikit-learn to call oneDAL behind the scenes.
The Python package depends on tbb (Intel Threading Building Blocks) for parallelization and is distributed as platform-specific wheels for x86_64 Linux (manylinux_2_28) and Windows. Installation has medium friction due to these binary constraints. The library is actively maintained (last release 2026-06-10, repository last commit 2026-08-14) and is classified as Production/Stable, making it suitable for production workloads where tabular ML performance is critical.
Use it for
- Accelerate scikit-learn pipelines by installing the Extension for Scikit-learn to call oneDAL routines transparently.
- Perform distributed K-means clustering and other algorithms across multi-node setups using oneDAL's MPI support.
- Speed up linear regression, random forest, and other tabular ML tasks on modern CPUs via SIMD and cache optimization.
- Leverage GPU acceleration for ML workloads on systems with SYCL-compatible GPUs using oneDAL's DPC++ interfaces.
- Integrate oneDAL C++ routines into Python data science applications for performance-critical tabular data processing.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
oneDAL is actively maintained, production-stable, and offers genuine performance benefits for tabular machine learning on CPUs and GPUs. However, the unclear Intel Simplified Software License requires verification before commercial use, and installation is limited to x86_64 Windows or manylinux_2_28 Linux. Most users will benefit more from the Extension for Scikit-learn (which uses oneDAL transparently) than from direct use of this package.
Install
daal on PyPI
Before you install
Medium install friction due to platform-specific wheels (manylinux_2_28_x86_64 and win_amd64) and a compiled runtime dependency on tbb. Active maintenance with recent releases and an engaged repository (651 stars, last commit 2026-08-14).
Requires tbb runtime dependency; limited to x86_64 Windows or manylinux_2_28 Linux platforms.
License in practice
Licensed under Intel Simplified Software License with unclear license treatment. Not an SPDX-recognized open-source license, so commercial or proprietary use may require explicit clarification with Intel before deployment.
Quickstart
pip install daal
import daal
# Access oneDAL routines via the daal module interfaceVerify before relying
- Exact Python version support range (requires_python unspecified in metadata)
- Whether the PyPI daal package is the primary Python distribution or a wrapper around the C++ library
- Performance gains relative to standard scikit-learn on typical hardware
- Compatibility matrix with specific GPU vendors and SYCL implementations
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagetbb |
| Maintenance | Actively maintained 65 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 170,425 / month, #10,396 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Other AudienceIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsLicense :: Other/Proprietary LicenseOperating System :: Microsoft :: WindowsTopic :: Software Development :: Libraries |
Evidence: daal-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; daal-2026.1.0-py2.py3-none-win_amd64.whl
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See also daal4py · onemkl-sycl-blas · onemkl-sycl-sparse · onemkl-sycl-dft · onemkl-sycl-lapack · mkl · tbb-devel · onemkl-sycl-rng · onemkl-license · oneccl-devel