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-mklMKL is only available on x86-64 architectures, excluding macOS systems. When using uv, one can
use platform markers to automatically install MKL-linked versions of NumPy and SciPy when on a
compatible system, and otherwise fall back to the default versions from PyPI.
To do so, copy the following into pyproject.toml and then run uv sync. This will install a
virtual environment in .venv, which can be activated on the command line or via most Python
editors.
[project]
name = "example-project"
version = "0.1.0"
requires-python = ">=3.13"
dependencies = [
"numpy>=2.2.6",
"scipy>=1.15.2",
"mkl-service>=2.4.2; platform_machine == 'x86_64' and sys_platform != 'darwin'",
]
[tool.uv.sources]
numpy = [{ index = "numpy-mkl", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" }]
scipy = [{ index = "numpy-mkl", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" }]
mkl-service = [{ index = "numpy-mkl", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" }]
[[tool.uv.index]]
name = "numpy-mkl"
url = "https://urob.github.io/numpy-mkl"
explicit = trueThe 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 2022. These compilers showed the most consistent runtime
performance in a series of benchmarks, even in comparison to
icx-compiled wheels.