---
id: onemkl-license
version: "2026.1.0"
license: Intel Simplified Software License
license_treatment: unclear
maintenance: active
---

# onemkl-license — Intel® oneAPI Math Kernel Library

License: unclear · Maintenance: active · Downloads: 383.3K/mo

## 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 above — verify before relying.

Provides C and Data Parallel C++ (DPC++) interfaces to Intel oneAPI Math Kernel Library for optimized mathematical computations on Intel CPUs and GPUs.

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

pip install onemkl-license
uv add onemkl-license
poetry add onemkl-license

## Installing onemkl-license

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.

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)

MKLROOT environment variable must be configured manually after installation; C or DPC++ compiler required to use the library interfaces.

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: Intel Simplified Software License (unclear)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 383.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

intel mkl math library, oneapi math kernel library, optimized linear algebra, intel cpu gpu math, dpc++ math routines, high-performance computing math, threaded math library, high-performance-computing, intel-hardware, numerical-computing

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