onemkl-sycl-rng
Intel® oneAPI Math Kernel Library
Decision gist · record as of 2026-08-14
Yes, if you are targeting Intel hardware and need optimized mathematical routines. The package is actively maintained, has no known vulnerabilities, and is in the top tier of PyPI packages by download volume. However, be prepared for medium install friction (three runtime dependencies) and manual MKLROOT configuration. Verify Python version compatibility and license terms for your use case before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires dpcpp-cpp-rt, intel-opencl-rt, and mkl runtime dependencies to be installed; MKLROOT environment variable must be configured manually post-installation.
- Medium install friction due to three runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl) that must be present.
- Package is actively maintained.
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License (proprietary, non-SPDX). License treatment is unclear, so users should review Intel's terms directly before integrating into commercial or open-source projects.
last release 2026-07-01 (44 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,683 downloads/mo, #10,359 on PyPI
Alternatives
Verify before relying
pip install onemkl-sycl-rng
# After installation, set MKLROOT environment variable
# (see Intel documentation for your platform)
import onemkl_sycl_rng- Exact Python version support (requires_python is unspecified in metadata)
- Whether C/DPC++ interfaces are directly callable from Python or require intermediate compilation
- Availability and completeness of random number generation routines in this release
What it is and what it does
onemkl-sycl-rng is Intel's Python binding to the oneAPI Math Kernel Library, specifically exposing C and Data Parallel C++ (DPC++) interfaces for mathematical operations optimized for Intel hardware. It targets developers building high-performance numerical applications that need to leverage Intel CPUs and GPUs through a Python interface.
The package ships as precompiled wheels for Linux and Windows x86-64 platforms and depends on three Intel runtime libraries (dpcpp-cpp-rt, intel-opencl-rt, mkl). A key gotcha is that the MKLROOT environment variable is not set automatically during PyPI installation and must be configured manually according to Intel's documentation. The package is marked Production/Stable and has been actively maintained.
Use it for
- Accelerate numerical simulations and linear algebra operations on Intel CPUs and GPUs from Python applications.
- Build high-performance scientific computing pipelines that require optimized math routines beyond standard libraries.
- Integrate Intel hardware-optimized random number generation into Monte Carlo or statistical modeling workflows.
- Develop machine learning or data science applications targeting Intel processors with native performance tuning.
- Port existing C/C++ oneAPI code to Python while preserving hardware-specific optimizations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are targeting Intel hardware and need optimized mathematical routines.
The package is actively maintained, has no known vulnerabilities, and is in the top tier of PyPI packages by download volume. However, be prepared for medium install friction (three runtime dependencies) and manual MKLROOT configuration. Verify Python version compatibility and license terms for your use case before committing.
Install
onemkl-sycl-rng on PyPI
Before you install
Medium install friction due to three runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl) that must be present. Package is actively maintained. Note that MKLROOT environment variable requires manual setup post-installation.
Requires dpcpp-cpp-rt, intel-opencl-rt, and mkl runtime dependencies to be installed; MKLROOT environment variable must be configured manually post-installation.
License in practice
Licensed under Intel Simplified Software License (proprietary, non-SPDX). License treatment is unclear, so users should review Intel's terms directly before integrating into commercial or open-source projects.
Quickstart
pip install onemkl-sycl-rng
# After installation, set MKLROOT environment variable
# (see Intel documentation for your platform)
import onemkl_sycl_rngVerify before relying
- Exact Python version support (requires_python is unspecified in metadata)
- Whether C/DPC++ interfaces are directly callable from Python or require intermediate compilation
- Availability and completeness of random number generation routines in this release
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesdpcpp-cpp-rtintel-opencl-rtmkl |
| Maintenance | Actively maintained 44 days since the last release |
| First released | |
| Downloads | 171,683 / month, #10,359 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 :: POSIX :: LinuxTopic :: Software Development :: Libraries |
Evidence: onemkl_sycl_rng-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; onemkl_sycl_rng-2026.1.0-py2.py3-none-win_amd64.whl
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See also mkl · onemkl-sycl-dft · onemkl-license · onemkl-sycl-blas · onemkl-sycl-lapack · mkl-include · onemkl-sycl-sparse · mkl-static · dpcpp-cpp-rt · pyudorandom