mkl

library2026.1.0pypypi✓ verified 22d ago

Intel® oneAPI Math Kernel Library (oneMKL) is a highly optimized, extensively threaded, and vectorized numerical library for mathematical functions. It provides a wide range of routines for linear algebra (BLAS, LAPACK, ScaLAPACK), fast Fourier transforms (FFT), vector math, and more. When used with Python libraries like NumPy and SciPy, it significantly accelerates numerical computations by providing highly optimized CPU-specific implementations. The `mkl` PyPI package, version 2025.3.1, typically provides the runtime components required for other Python packages to link against and utilize MKL, with updates usually following major oneAPI releases.

ai-ml data serialization

pip install mkl

package ↗homepage ↗

INSTALL

IMPORT

SIG · MKL

mkl

ai-mlpythonv2026.1.0

Install

9.3s avg

Import

—

Disk

16MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2026.1.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 9.3s · import 0.000s · 19MB

16MB installed

● package 16MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

get_version

✓ from mkl import get_version

✗ import mkl; mkl.get_version()

This quickstart demonstrates how to install the MKL runtime and verify that it is being used by common numerical libraries like NumPy. It checks the MKL Python package version, detailed MKL service info, and then inspects NumPy's configuration to confirm MKL linkage before performing a basic benchmark.

import mkl import numpy as np print("MKL Python package version:", mkl.get_version()) print("\nMKL Service info:") print(mkl.service.get_mkl_info()) # Verify if NumPy is using MKL print("\nNumPy configuration (look for 'mkl' or 'blas_mkl'):") np.__config__.show() # Perform a simple matrix multiplication which should be accelerated by MKL a = np.random.rand(1000, 1000) b = np.random.rand(1000, 1000) print("\nPerforming a matrix multiplication (1000x1000) with NumPy...") _ = a @ b print("Operation complete. Check NumPy config above to see MKL linkage.")

Debug

Known issues

gotchaMKL can conflict with other BLAS/LAPACK implementations (e.g., OpenBLAS, BLIS) if multiple are installed or linked incorrectly. This can lead to unexpected performance, crashes, or incorrect results.

fix

Ensure only one BLAS/LAPACK provider is active in your environment, or explicitly configure your numerical libraries to use a specific backend. Using a tool like `conda` can help manage these dependencies more robustly.

affects: All versions

gotchaIt's common for users to `pip install mkl` but not verify that their numerical libraries (NumPy, SciPy) are actually using MKL, leading to no performance gains. MKL needs to be correctly linked by these libraries.

fix

After installation, use `import numpy; numpy.__config__.show()` or `import mkl; mkl.service.get_mkl_info()` to confirm MKL linkage. Ensure your Python environment's `PATH` (Windows) or `LD_LIBRARY_PATH` (Linux/macOS) correctly points to MKL libraries if issues persist.

affects: All versions

gotchaThe `mkl` package on PyPI provides MKL runtime components, but system-wide MKL installations (e.g., via Intel oneAPI base toolkit) or Conda environments with `mkl` (e.g., `conda install numpy scipy mkl`) can also provide MKL. Mixing these can lead to conflicts, library loading issues, or unstable behavior.

fix

Prefer a consistent installation method for MKL and related Python numerical libraries. For example, use all `pip`, all `conda`, or rely solely on Intel's official distributions to avoid environment conflicts.

affects: All versions

Upgrade

Version history

2026.1.0latest on PyPI · released Jul 1, 2026

Audit

Dependencies

No dependency data recorded yet.

Agent activity

7 hits · last 30 days

node

Resources

packagehttps://pypi.org/project/mkl/ ↗

homepagehttps://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html ↗