Observations & Experiments by Hari Thapliyal

Setup

• Forked from https://github.com/dasarpai/intel-scikit-learn-intelex
• Download via github desktop on my windows11
• cd intel-scikit-learn-intelex
• created virtual .venv
• pip install -r requirements-doc.txt
• created requirements-extra.txt
• pip install -r requirements-extra.txt

Experiments / Run

Run examples\notebooks\linear_regression.ipynb

Compare MSE metric of patched Scikit-learn and original

MSE metric of patched Scikit-learn: 0.771676778793335
MSE metric of unpatched Scikit-learn: 0.7716856598854065
Metrics ratio: 0.9999884963035583

With Scikit-learn-intelex patching you can:

Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code); Fast execution training and prediction of Scikit-learn models;
Get the similar quality
Get speedup in 118.4 times.

Run examples\notebooks\nusvr_medical_charges.ipynb

Compare R2 score of patched Scikit-learn and original

R2 score of patched Scikit-learn: 0.8595757545754246
R2 score of unpatched Scikit-learn: 0.8596252069063628
Metrics ratio: 0.9999424722186588

With Scikit-learn-intelex patching you can:

Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);
Fast execution training and prediction of Scikit-learn models;
Get the similar quality
Get speedup in 1.9 times.

Run examples\notebooks\knn_mnist.ipynb

With scikit-learn-intelex patching you can:

Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);
Fast execution training and prediction of Scikit-learn models;
Get the similar quality
Get speedup in 2.3 times.

examples\notebooks\svc_adult.ipynb

With scikit-learn-intelex patching you can:

Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);
Fast execution training and prediction of Scikit-learn models;
Get the similar quality
Get speedup in 7.0 times.

Intel(R) Extension for Scikit-learn*

Speed up your scikit-learn applications for Intel(R) CPUs and GPUs across single- and multi-node configurations

Releases   |   Documentation   |   Examples   |   Support   |  License   

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Overview

Intel(R) 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 for all upcoming Intel(R) platforms at launch time.

With Intel(R) Extension for Scikit-learn, you can:

• Speed up training and inference by up to 100x with the equivalent mathematical accuracy
• Benefit from performance improvements across different Intel(R) hardware 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

Intel(R) Extension for Scikit-learn is also a part of Intel(R) AI Tools.

Acceleration

Benchmarks code

Intel(R) Optimizations

• Enable Intel(R) CPU optimizations

  import numpy as np
  from sklearnex import patch_sklearn
  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)
  clustering = DBSCAN(eps=3, min_samples=2).fit(X)

• Enable Intel(R) GPU optimizations

  import numpy as np
  import dpctl
  from sklearnex import patch_sklearn, config_context
  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 config_context(target_offload="gpu:0"):
      clustering = DBSCAN(eps=3, min_samples=2).fit(X)

👀 Check out available notebooks for more examples.

Installation

To install Intel(R) Extension for Scikit-learn, run:

pip install scikit-learn-intelex

See all installation instructions in the Installation Guide.

Integration

The software acceleration is achieved through patching. It means, replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.

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:

  python -m sklearnex my_application.py
  

• Add the following lines to the script:

  from sklearnex import patch_sklearn
  patch_sklearn()
  

👀 Read about other ways to patch scikit-learn.

Documentation

• Quick Start
• Documentation and Tutorials
• Release Notes
• Medium Blogs
• Code of Conduct

daal4py and oneDAL

The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Learn more:

• About Intel(R) oneAPI Data Analytics Library
• About daal4py

Samples & Examples

• Examples
• Samples
• Kaggle Kernels

How to Contribute

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

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* 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.