onemkl-license
Intel® oneAPI Math Kernel Library
Decision gist · record as of 2026-08-14
Yes, if you are building C or C++ applications targeting Intel CPUs or GPUs and need highly optimized mathematical routines. The package is actively maintained with no known vulnerabilities. However, be aware of the proprietary license (review Intel's terms), the manual MKLROOT configuration requirement, and that this is a library package requiring C/DPC++ compilation skills, not a pure Python tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- MKLROOT environment variable must be configured manually after installation; C or DPC++ compiler required to use the library interfaces.
- Medium install friction; wheels are available for Linux and Windows x86_64.
- The package is actively maintained with a recent release.
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License with unclear treatment. This is a proprietary license not mapped to a standard SPDX identifier, so review Intel's terms directly before use in commercial or redistributed contexts.
last release 2026-07-01 (44 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 383,288 downloads/mo, #7,078 on PyPI
Alternatives
Verify before relying
# Install
pip install onemkl-license==2026.1.0
# Set MKLROOT environment variable (required)
export MKLROOT=/path/to/mkl
# Then import and use in C/C++ or DPC++ code
# (This is a library package; usage is via C/DPC++ interfaces, not Python directly)- Whether this package installs the actual MKL binaries or only license/header files.
- Exact scope of C and DPC++ interface coverage compared to full Intel MKL.
- Whether Python version support is truly unspecified or inherited from dependencies.
- What 'medium' install friction specifically entails beyond wheel availability.
What it is and what it does
This package provides C and Data Parallel C++ (DPC++) language bindings to Intel oneAPI Math Kernel Library, a collection of highly optimized mathematical routines for scientific and high-performance computing workloads. It is designed to enable developers to call MKL functions from C, C++, or other languages that can interface with C code, targeting both current and future Intel CPU and GPU architectures.
The package is distributed as wheels for Linux and Windows x86_64 platforms. A key constraint is that the MKLROOT environment variable must be configured manually after installation—this is not handled automatically by the PyPI installer. Developers integrating this package need to set up this variable and have a C or DPC++ compiler available to build and link against the library interfaces.
Use it for
- Accelerate numerical computations in scientific applications by linking C/C++ code to optimized MKL routines.
- Build high-performance linear algebra and matrix operations for machine learning or data analysis pipelines.
- Optimize mathematical kernels for Intel GPU execution using DPC++ interfaces.
- Integrate MKL into heterogeneous CPU-GPU compute workflows targeting Intel hardware.
- Develop performance-critical numerical libraries that require fine-grained control over math kernel selection.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building C or C++ applications targeting Intel CPUs or GPUs and need highly optimized mathematical routines.
The package is actively maintained with no known vulnerabilities. However, be aware of the proprietary license (review Intel's terms), the manual MKLROOT configuration requirement, and that this is a library package requiring C/DPC++ compilation skills, not a pure Python tool.
Install
onemkl-license on PyPI
Before you install
Medium install friction; wheels are available for Linux and Windows x86_64. The package is actively maintained with a recent release. Note that MKLROOT environment variable must be configured manually post-installation—this is not set up automatically by PyPI.
MKLROOT environment variable must be configured manually after installation; C or DPC++ compiler required to use the library interfaces.
License in practice
Licensed under Intel Simplified Software License with unclear treatment. This is a proprietary license not mapped to a standard SPDX identifier, so review Intel's terms directly before use in commercial or redistributed contexts.
Quickstart
# Install
pip install onemkl-license==2026.1.0
# Set MKLROOT environment variable (required)
export MKLROOT=/path/to/mkl
# Then import and use in C/C++ or DPC++ code
# (This is a library package; usage is via C/DPC++ interfaces, not Python directly)Verify before relying
- Whether this package installs the actual MKL binaries or only license/header files.
- Exact scope of C and DPC++ interface coverage compared to full Intel MKL.
- Whether Python version support is truly unspecified or inherited from dependencies.
- What 'medium' install friction specifically entails beyond wheel availability.
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 44 days since the last release |
| First released | |
| Downloads | 383,288 / month, #7,078 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_license-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; onemkl_license-2026.1.0-py2.py3-none-win_amd64.whl
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See also mkl · onemkl-sycl-dft · onemkl-sycl-rng · mkl-include · mkl-static · onemkl-sycl-blas · onemkl-sycl-lapack · onemkl-sycl-sparse · dpcpp-cpp-rt · intel-cmplr-lib-ur