mkl-static
Intel® oneAPI Math Kernel Library
Decision gist · record as of 2026-08-14
Yes, if you need Intel MKL's optimized math routines in a C/C++ or DPC++ application and are willing to manually configure MKLROOT after installation. The package is actively maintained, has no known vulnerabilities, and receives substantial downloads. The proprietary license and unclear license treatment warrant review before commercial use. Medium install friction is acceptable for the performance gains in numerical computing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- MKLROOT environment variable must be set manually post-installation; requires knowledge of Intel MKL C interface usage patterns.
- Medium install friction due to three runtime dependencies (intel-openmp, tbb-devel, mkl-include) and platform-specific wheels.
- Package is actively maintained with a recent release on 2026-07-01.
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License (proprietary, not SPDX-identified). License treatment is unclear—review Intel's terms before use in commercial or redistributed contexts.
last release 2026-07-01 (44 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 141,050 downloads/mo, #11,255 on PyPI
Alternatives
Verify before relying
pip install mkl-static
# After installation, set MKLROOT environment variable manually
# (not configured automatically by PyPI installation)
import ctypes
# Link against MKL C interfaces via ctypes or compiled code- Exact Python version support (requires_python is unspecified in metadata)
- Whether tbb-devel and mkl-include are automatically resolved or require manual system setup
- Scope of GPU support and which Intel GPU architectures are covered
- Whether static linking avoids runtime library discovery overhead as claimed
What it is and what it does
mkl-static packages Intel's oneAPI Math Kernel Library as a Python-installable wheel, exposing C and Data Parallel C++ (DPC++) interfaces for high-performance mathematical computing. It is designed for applications requiring optimized mathematical routines on Intel processors and compatible GPUs.
The package depends on intel-openmp, tbb-devel, and mkl-include at runtime. Installation is straightforward via pip, but the MKLROOT environment variable must be configured manually afterward—it is not set automatically by the PyPI installation. Users typically call MKL routines through C interfaces via ctypes or compiled extensions rather than through direct Python APIs.
Use it for
- Accelerate numerical computations in scientific Python applications by linking to optimized MKL routines from C/C++ code.
- Build high-performance machine learning or data science pipelines that require fast mathematical operations on Intel hardware.
- Develop GPU-accelerated applications using Data Parallel C++ (DPC++) that target Intel processors and GPUs.
- Support research or engineering workflows requiring mathematical operations with Intel CPU/GPU tuning.
- Integrate MKL's threaded routines into existing C/C++ codebases for performance-critical sections.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need Intel MKL's optimized math routines in a C/C++ or DPC++ application and are willing to manually configure MKLROOT after installation.
The package is actively maintained, has no known vulnerabilities, and receives substantial downloads. The proprietary license and unclear license treatment warrant review before commercial use. Medium install friction is acceptable for the performance gains in numerical computing.
Install
mkl-static on PyPI
Before you install
Medium install friction due to three runtime dependencies (intel-openmp, tbb-devel, mkl-include) and platform-specific wheels. Package is actively maintained with a recent release on 2026-07-01.
MKLROOT environment variable must be set manually post-installation; requires knowledge of Intel MKL C interface usage patterns.
License in practice
Licensed under Intel Simplified Software License (proprietary, not SPDX-identified). License treatment is unclear—review Intel's terms before use in commercial or redistributed contexts.
Quickstart
pip install mkl-static
# After installation, set MKLROOT environment variable manually
# (not configured automatically by PyPI installation)
import ctypes
# Link against MKL C interfaces via ctypes or compiled codeVerify before relying
- Exact Python version support (requires_python is unspecified in metadata)
- Whether tbb-devel and mkl-include are automatically resolved or require manual system setup
- Scope of GPU support and which Intel GPU architectures are covered
- Whether static linking avoids runtime library discovery overhead as claimed
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesintel-openmptbb-develmkl-include |
| Maintenance | Actively maintained 44 days since the last release |
| First released | |
| Downloads | 141,050 / month, #11,255 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: mkl_static-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; mkl_static-2026.1.0-py2.py3-none-win_amd64.whl
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See also mkl · mkl-include · onemkl-license · onemkl-sycl-dft · onemkl-sycl-rng · onemkl-sycl-lapack · onemkl-sycl-blas · onemkl-sycl-sparse · dpcpp-cpp-rt · intel-sycl-rt