mkl_umath

mkl_umath._ufuncs exposes Intel® OneAPI Math Kernel Library (OneMKL) powered version of loops used in the patched version of NumPy, that used to be included in Intel® Distribution for Python*.

Patches were factored out per community feedback (NEP-36).

mkl_umath started as a part of Intel® Distribution for Python* optimizations to NumPy, and is now being released as a stand-alone package. It can be installed into conda environment using:

   conda install -c https://software.repos.intel.com/python/conda mkl_umath

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To install mkl_umath PyPI package please use following command:

   python -m pip install --i https://software.repos.intel.com/python/pypi -extra-index-url https://pypi.org/simple mkl_umath

If command above installs NumPy package from the PyPI, please use the following command to install Intel optimized NumPy wheel package from Intel PyPI Cloud:

   python -m pip install --i https://software.repos.intel.com/python/pypi -extra-index-url https://pypi.org/simple mkl_umath numpy==<numpy_version>

Where <numpy_version> should be the latest version from https://software.repos.intel.com/python/conda/

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Patching Mechanisms

mkl_umath provides convenient patch methods to enable MKL-accelerated umath operations in NumPy with or without modifying your code.

CLI Quickstart

Persistent patch (all Python sessions)

# Install
python -m mkl_umath --patch install

# Status (exit code: 0 = installed, 1 = not installed)
python -m mkl_umath --patch status

# Remove
python -m mkl_umath --patch uninstall

Verify patch state

python -c "import mkl_umath; print(f'mkl_umath.is_patched(): {mkl_umath.is_patched()}')"

One-shot patch (single command only)

# Script
python -m mkl_umath --with-numpy-patch my_script.py

# Pytest
python -m mkl_umath --with-numpy-patch -m pytest tests/

# One-liner
python -m mkl_umath --with-numpy-patch -c "import mkl_umath; print(f\"mkl_umath.is_patched(): {mkl_umath.is_patched()}\")"

# Non-Python command
python -m mkl_umath --with-numpy-patch -- <command> [args...]

Programmatic Quickstart

import mkl_umath
import numpy

mkl_umath.patch_numpy_umath()
print(mkl_umath.is_patched())
# run your accelerated numpy workloads here!
mkl_umath.restore_numpy_umath()

import mkl_umath
import numpy
with mkl_umath.mkl_umath():
   # run your accelerated workloads here!
   pass

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Building

Intel(R) C compiler and Intel(R) OneAPI Math Kernel Library (OneMKL) are required to build mkl_umath from source.

If these are installed as part of a oneAPI installation, the following packages must also be installed into the environment

• cmake
• ninja
• cython
• scikit-build
• numpy

If build dependencies are to be installed with Conda, the following packages must be installed from the Intel(R) channel

• mkl-devel
• dpcpp_linux-64 (or dpcpp_win-64 for Windows)
• numpy-base

then the remaining dependencies

• cmake
• ninja
• cython
• scikit-build

and for mkl-devel and dpcpp_linux-64 in a Conda environment, MKLROOT environment variable must be set On Linux

export MKLROOT=$CONDA_PREFIX

On Windows

set MKLROOT=%CONDA_PREFIX%

If using oneAPI, it must be activated in the environment

On Linux

source ${ONEAPI_ROOT}/setvars.sh

On Windows

call "%ONEAPI_ROOT%\setvars.bat"

finally, execute

CC=icx pip install --no-build-isolation --no-deps .