This repository provides binary wheels for NumPy and SciPy, linked to Intel's high-performance
oneAPI Math Kernel
Library for Intel CPUs.
The wheels are accessible through a custom Python Package Index (PyPI) and can be installed with
pip or uv.
MKL-accelerated wheels are available for 64-bit versions of Linux and Windows. There are no
prerequisites apart from pip or uv; all dependencies are automatically installed by the package
manager.
uv
# Run this from project directory
uv init
uv add numpy scipy --index https://urob.github.io/numpy-mklpip
pip install numpy scipy --extra-index-url https://urob.github.io/numpy-mklThe usual way to obtain MKL-accelerated NumPy and SciPy packages is through
Anaconda or Conda-forge. The purpose of
this repository is to provide an alternative for users who prefer to use pip or uv for package
management. Other alternatives are listed below.
| MKL | PyPI | Notes | |
|---|---|---|---|
| This repository | Yes | Yes | |
| Intel(r) Distribution for Python | Yes | Yes | Does not support NumPy 2.x |
| Numpy-mkl-wheels | Yes | No | No Linux wheels |
| Python Package Index | No | Yes | Slow on Intel CPUs |
Linux wheels are built with gcc on Ubuntu 22.04. Windows wheels are built with msvc (numpy) and
mingw-w64 (scipy) on Windows Server 2019. These compilers showed the most consistent runtime
performance in a series of benchmarks, even in comparison to
icx-compiled wheels.
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