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https://github.com/intelpython/mkl_umath

Package implementing NumPy's UFuncs based on SVML and MKL VML

https://github.com/intelpython/mkl_umath

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.5%) to scientific vocabulary

Keywords

mkl numpy python

Keywords from Contributors

archival interactive sequences projection shellcodes modular network-simulation charts tracing versions
Last synced: 6 months ago · JSON representation

Repository

Package implementing NumPy's UFuncs based on SVML and MKL VML

Basic Info
  • Host: GitHub
  • Owner: IntelPython
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Size: 646 KB
Statistics
  • Stars: 3
  • Watchers: 6
  • Forks: 5
  • Open Issues: 3
  • Releases: 6
Topics
mkl numpy python
Created almost 5 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License Codeowners Security

README.md

Conda package OpenSSF Scorecard

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

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/

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 - tbb-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, tbb-devel and dpcpp_linux-64 in a Conda environment, MKLROOT environment variable must be set On Linux sh export MKLROOT=$CONDA_PREFIX

On Windows sh 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 .

Owner

  • Name: Intel Python
  • Login: IntelPython
  • Kind: organization
  • Email: scripting@intel.com
  • Location: Austin, TX

Examples and other resources for Intel Distribution for Python

GitHub Events

Total
  • Create event: 93
  • Release event: 3
  • Issues event: 4
  • Watch event: 1
  • Delete event: 91
  • Member event: 3
  • Issue comment event: 64
  • Push event: 369
  • Pull request review comment event: 38
  • Pull request review event: 104
  • Pull request event: 123
  • Fork event: 2
Last Year
  • Create event: 93
  • Release event: 3
  • Issues event: 4
  • Watch event: 1
  • Delete event: 91
  • Member event: 3
  • Issue comment event: 64
  • Push event: 369
  • Pull request review comment event: 38
  • Pull request review event: 104
  • Pull request event: 123
  • Fork event: 2

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 137
  • Total Committers: 11
  • Avg Commits per committer: 12.455
  • Development Distribution Score (DDS): 0.642
Past Year
  • Commits: 87
  • Committers: 5
  • Avg Commits per committer: 17.4
  • Development Distribution Score (DDS): 0.713
Top Committers
Name Email Commits
Oleksandr Pavlyk o****k@i****m 49
dependabot[bot] 4****] 24
Nikita Grigorian n****n@i****m 20
amakarye a****v@i****m 13
Komarova, Evseniia e****a@i****m 12
Vahid Tavanashad 1****a 10
Andres Guzman-Ballen a****n@i****m 3
Stewart Blacklock s****k@i****m 2
Alexander Rybkin a****n@i****m 2
Vyacheslav Smirnov v****v@i****m 1
alexander.makaryev a****e@f****m 1
Committer Domains (Top 20 + Academic)
intel.com: 8 fxsatlin01.an.intel.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 6
  • Total pull requests: 136
  • Average time to close issues: 3 months
  • Average time to close pull requests: 10 days
  • Total issue authors: 5
  • Total pull request authors: 6
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.66
  • Merged pull requests: 93
  • Bot issues: 1
  • Bot pull requests: 53
Past Year
  • Issues: 4
  • Pull requests: 131
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 6 days
  • Issue authors: 3
  • Pull request authors: 5
  • Average comments per issue: 0.75
  • Average comments per pull request: 0.69
  • Merged pull requests: 88
  • Bot issues: 1
  • Bot pull requests: 53
View more stats
Top Authors
Issue Authors
  • ndgrigorian (2)
  • chillenb (1)
  • dependabot[bot] (1)
  • oleksandr-pavlyk (1)
  • samaid (1)
Pull Request Authors
  • dependabot[bot] (67)
  • vtavana (32)
  • ndgrigorian (25)
  • ekomarova (24)
  • oleksandr-pavlyk (13)
  • xaleryb (1)
Top Labels
Issue Labels
dependencies (1)
Pull Request Labels
dependencies (67) github_actions (24)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 10,283 last-month
  • Total docker downloads: 930
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 4
    (may contain duplicates)
  • Total versions: 6
  • Total maintainers: 2
pypi.org: mkl-umath

Intel (R) MKL-based universal functions for NumPy arrays

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 3
  • Downloads: 10,283 Last month
  • Docker Downloads: 930
Rankings
Docker downloads count: 1.9%
Downloads: 5.7%
Dependent repos count: 8.9%
Dependent packages count: 10.1%
Average: 14.1%
Forks count: 19.1%
Stargazers count: 38.8%
Maintainers (2)
sat-bot Intel-Python
Last synced: 6 months ago
anaconda.org: mkl_umath

Universal functions for real and complex floating point arrays powered by Intel(R) Math Kernel Library Vector (Intel(R) MKL) and Intel(R) Short Vector Math Library (Intel(R) SVML)

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
Rankings
Dependent packages count: 51.0%
Dependent repos count: 51.2%
Average: 54.7%
Forks count: 58.3%
Stargazers count: 58.3%
Last synced: 6 months ago

Dependencies

setup.py pypi
  • numpy *
mkl_umath/setup.py pypi