Intel(R) Extension for Scikit-learn*

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

🛠 Installation

Intel(R) Extension for Scikit-learn is available at the Python Package Index, in Conda-Forge and in Intel's conda channel. Intel(R) Extension for Scikit-learn is also available as a part of Intel® oneAPI AI Analytics Toolkit (AI Kit).

To install through pip:

pip install scikit-learn-intelex

See the documentation for more details about supported platforms and other ways of installing it.

You can build the package from sources as well.

⚡️ Get Started

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

• Enable 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 GPU optimizations

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

import numpy as np
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)

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:

  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:

  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 config_context(target_offload="gpu:0"):
      clustering = DBSCAN(eps=3, min_samples=2).fit(X)
  

🚀 Scikit-learn patching

Configurations:

• HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket
• SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8

Benchmarks code

Intel(R) Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, submit an issue on GitHub.

Read more about it in the documentation for scikit-learn patching.

👀 Follow us on Medium

We publish blogs on Medium, so follow us to learn tips and tricks for more efficient data analysis with the help of Intel(R) Extension for Scikit-learn. Here are our latest blogs:

• Save Time and Money with Intel Extension for Scikit-learn
• Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors
• Leverage Intel Optimizations in Scikit-Learn
• Intel Gives Scikit-Learn the Performance Boost Data Scientists Need
• From Hours to Minutes: 600x Faster SVM
• Improve the Performance of XGBoost and LightGBM Inference
• Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit
• Accelerate Your scikit-learn Applications
• Accelerate Linear Models for Machine Learning
• Accelerate K-Means Clustering

🔗 Important links

• Notebook examples
• Documentation
• scikit-learn API and patching
• Benchmark code
• Building from Sources
• About Intel(R) oneAPI Data Analytics Library

💬 Support

Report issues, ask questions, and provide suggestions using:

• GitHub Issues
• GitHub Discussions
• Forum

You may reach out to project maintainers privately at onedal.maintainers@intel.com