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https://github.com/uxlfoundation/scikit-learn-intelex

Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

https://github.com/uxlfoundation/scikit-learn-intelex

Science Score: 36.0%

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    1 of 84 committers (1.2%) from academic institutions
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    Low similarity (12.6%) to scientific vocabulary

Keywords

ai-inference ai-machine-learning ai-training analytics big-data data-analysis gpu machine-learning machine-learning-algorithms oneapi python scikit-learn swrepo

Keywords from Contributors

deep-neural-networks distributed mot transformer interaction parallel openmp notebooks reinforcement-learning cryptocurrency
Last synced: 5 months ago · JSON representation

Repository

Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Basic Info
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  • Stars: 1,306
  • Watchers: 24
  • Forks: 183
  • Open Issues: 74
  • Releases: 38
Topics
ai-inference ai-machine-learning ai-training analytics big-data data-analysis gpu machine-learning machine-learning-algorithms oneapi python scikit-learn swrepo
Created over 7 years ago · Last pushed 6 months ago
Metadata Files
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README.md

Extension for Scikit-learn*

Speed up your scikit-learn applications for CPUs and GPUs across single- and multi-node configurations [Releases](https://github.com/uxlfoundation/scikit-learn-intelex/releases)   |   [Documentation](https://uxlfoundation.github.io/scikit-learn-intelex/)   |   [Examples](https://github.com/uxlfoundation/scikit-learn-intelex/tree/master/examples/notebooks)   |   [Support](SUPPORT.md)   |  [License](https://github.com/uxlfoundation/scikit-learn-intelex/blob/master/LICENSE)    [![Build Status](https://dev.azure.com/daal/daal4py/_apis/build/status/CI?branchName=main)](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=main) [![Coverity Scan Build Status](https://scan.coverity.com/projects/21716/badge.svg)](https://scan.coverity.com/projects/daal4py) [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/uxlfoundation/scikit-learn-intelex/badge)](https://securityscorecards.dev/viewer/?uri=github.com/uxlfoundation/scikit-learn-intelex) [![Join the community on GitHub Discussions](https://badgen.net/badge/join%20the%20discussion/on%20github/black?icon=github)](https://github.com/uxlfoundation/scikit-learn-intelex/discussions) [![PyPI Version](https://img.shields.io/pypi/v/scikit-learn-intelex)](https://pypi.org/project/scikit-learn-intelex/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/scikit-learn-intelex)](https://anaconda.org/conda-forge/scikit-learn-intelex) [![python version](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue) [![scikit-learn supported versions](https://img.shields.io/badge/sklearn-1.0%20%7C%201.4%20%7C%201.5%20%7C%201.6%20%7C%201.7-blue)](https://img.shields.io/badge/sklearn-1.0%20%7C%201.4%20%7C%201.5%20%7C%201.6%20%7C%201.7-blue) ---

Overview

Extension for Scikit-learn is a free software AI accelerator designed to deliver over 10-100X acceleration to your existing scikit-learn code. The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations.

With Extension for Scikit-learn, you can:

  • Speed up training and inference by up to 100x with equivalent mathematical accuracy
  • Benefit from performance improvements across different hardware configurations, including GPUs and multi-GPU configurations
  • Integrate the extension into your existing Scikit-learn applications without code modifications
  • Continue to use the open-source scikit-learn API
  • Enable and disable the extension with a couple of lines of code or at the command line

Acceleration

Benchmarks code

Optimizations

Easiest way to benefit from accelerations from the extension is by patching scikit-learn with it:

  • Enable CPU optimizations

    ```python import numpy as np from sklearnex import patchsklearn patchsklearn()

    from sklearn.cluster import DBSCAN

    X = np.array([[1., 2.], [2., 2.], [2., 3.], [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32) clustering = DBSCAN(eps=3, min_samples=2).fit(X) ```

  • Enable GPU optimizations

    Note: executing on GPU has additional system software requirements - see details.

    ```python import numpy as np from sklearnex import patchsklearn, configcontext patch_sklearn()

    from sklearn.cluster import DBSCAN

    X = np.array([[1., 2.], [2., 2.], [2., 3.], [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32) with configcontext(targetoffload="gpu:0"): clustering = DBSCAN(eps=3, min_samples=2).fit(X) ```

:eyes: Check out available notebooks for more examples.

Usage without patching

Alternatively, all functionalities are also available under a separate module which can be imported directly, without involving any patching.

  • To run on CPU:

```python import numpy as np from sklearnex.cluster import DBSCAN

X = np.array([[1., 2.], [2., 2.], [2., 3.], [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32) clustering = DBSCAN(eps=3, min_samples=2).fit(X) ```

  • To run on GPU:

```python import numpy as np from sklearnex import config_context from sklearnex.cluster import DBSCAN

X = np.array([[1., 2.], [2., 2.], [2., 3.], [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32) with configcontext(targetoffload="gpu:0"): clustering = DBSCAN(eps=3, min_samples=2).fit(X) ```

Installation

To install Extension for Scikit-learn, run:

shell pip install scikit-learn-intelex

Package is also offered through other channels such as conda-forge. See all installation instructions in the Installation Guide.

Integration

The easiest way of accelerating scikit-learn workflows with the extension is through through patching, which replaces the stock scikit-learn algorithms with their optimized versions provided by the extension using the same namespaces in the same modules as scikit-learn.

The patching only affects supported algorithms and their parameters. You can still use not supported ones in your code, the package simply fallbacks into the stock version of scikit-learn.

TIP: Enable verbose mode to see which implementation of the algorithm is currently used.

To patch scikit-learn, you can: * Use the following command-line flag: shell python -m sklearnex my_application.py * Add the following lines to the script: python from sklearnex import patch_sklearn patch_sklearn()

:eyes: Read about other ways to patch scikit-learn.

As an alternative, accelerated classes from the extension can also be imported directly without patching, thereby allowing to keep them separate from stock scikit-learn ones - for example:

```python from sklearnex.cluster import DBSCAN as exDBSCAN from sklearn.cluster import DBSCAN as stockDBSCAN

...

```

Documentation

Extension and oneDAL

Acceleration in patched scikit-learn classes is achieved by replacing calls to scikit-learn with calls to oneDAL (oneAPI Data Analytics Library) behind the scenes: - oneAPI Data Analytics Library

Samples & Examples

How to Contribute

We welcome community contributions, check our Contributing Guidelines to learn more.

* The Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

Owner

  • Name: United Acceleration (UXL) Foundation
  • Login: uxlfoundation
  • Kind: organization
  • Location: United States of America

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 2,039
  • Total Committers: 84
  • Avg Commits per committer: 24.274
  • Development Distribution Score (DDS): 0.837
Past Year
  • Commits: 479
  • Committers: 24
  • Avg Commits per committer: 19.958
  • Development Distribution Score (DDS): 0.729
Top Committers
Name Email Commits
renovate[bot] 2****] 332
Frank Schlimbach f****h@i****m 236
Ian Faust i****t@g****m 158
Oleksandr Pavlyk o****k@i****m 133
Alexander Andreev a****v@i****m 130
Kirill k****v@i****m 113
amakarye a****v@i****m 104
Pavel Yakovlev 3****h 89
david-cortes-intel d****s@i****m 85
Samir Nasibli s****i@i****m 85
Nikolay Petrov n****v@i****m 72
ethanglaser 4****r 69
Kulandin Denis d****n@i****m 49
Andreas Huber 9****1 33
Nikita Timakin n****n@i****m 31
olegkkruglov 1****v 28
Michael Smirnov m****v@i****m 21
Ekaterina Mekhnetsova m****a@g****m 15
Alexandra a****a@i****m 15
Anatoly Volkov 1****l 13
Andrey Gorshkov a****v@i****m 13
msa 1****m 13
KalyanovD d****v@i****m 12
KulikovNikita n****v@i****m 12
Victoriya Fedotova v****a@i****m 10
Dmitry Zagorny d****y@i****m 9
Dmitry Razdoburdin d****n@i****m 8
dependabot[bot] 4****] 8
Ben Moore b****e@i****m 8
Maria Petrova m****a@i****m 8
and 54 more...
Committer Domains (Top 20 + Academic)
intel.com: 35 mail.ru: 1 snyk.io: 1 nntvml218.inn.intel.com: 1 aliyun.com: 1 email.wm.edu: 1 ensta.org: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 58
  • Total pull requests: 863
  • Average time to close issues: over 1 year
  • Average time to close pull requests: 20 days
  • Total issue authors: 25
  • Total pull request authors: 33
  • Average comments per issue: 1.9
  • Average comments per pull request: 2.62
  • Merged pull requests: 535
  • Bot issues: 0
  • Bot pull requests: 256
Past Year
  • Issues: 15
  • Pull requests: 773
  • Average time to close issues: 28 days
  • Average time to close pull requests: 8 days
  • Issue authors: 6
  • Pull request authors: 26
  • Average comments per issue: 0.4
  • Average comments per pull request: 2.59
  • Merged pull requests: 474
  • Bot issues: 0
  • Bot pull requests: 256
View more stats
Top Authors
Issue Authors
  • fschlimb (15)
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Pull Request Authors
  • renovate[bot] (238)
  • david-cortes-intel (214)
  • icfaust (130)
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Top Labels
Issue Labels
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Pull Request Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 104,614 last-month
  • Total docker downloads: 14,109,203
  • Total dependent packages: 18
  • Total dependent repositories: 615
  • Total versions: 40
  • Total maintainers: 2
pypi.org: scikit-learn-intelex

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application.

  • Versions: 40
  • Dependent Packages: 18
  • Dependent Repositories: 615
  • Downloads: 104,614 Last month
  • Docker Downloads: 14,109,203
Rankings
Dependent repos count: 0.6%
Downloads: 0.6%
Dependent packages count: 0.7%
Docker downloads count: 1.1%
Average: 1.5%
Stargazers count: 2.0%
Forks count: 3.9%
Maintainers (2)
IntelAutomationEngineering sat-bot
Last synced: 6 months ago

Dependencies

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  • Cython ==0.29.24
  • Cython ==0.29.25
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requirements-doc.txt pypi
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  • importlib-metadata ==4.8.2
  • importlib-resources ==5.4.0
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requirements-dppy.txt pypi
  • dpctl ==0.8.0
requirements-test.txt pypi
  • pandas ==1.4.0
  • pandas ==1.2.2
  • pandas ==1.1.5
  • pytest ==6.2.5
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requirements.txt pypi
  • daal ==2021.4.0
  • numpy >=1.15