scikit-learn-intelex 2023.2.1

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

Intel(R) Extension for Scikit-learn*

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application. The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Patching scikit-learn makes it a well-suited machine learning framework for dealing with real-life problems.

⚠️Intel(R) Extension for Scikit-learn contains scikit-learn patching functionality that was originally available in daal4py package. All future updates for the patches will be available only in Intel(R) Extension for Scikit-learn. We recommend you to use scikit-learn-intelex package instead of daal4py. You can learn more about daal4py in daal4py documentation.

Running the latest scikit-learn test suite with Intel(R) Extension for Scikit-learn:

👀 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
• About Intel(R) daal4py

💬 Support

Report issues, ask questions, and provide suggestions using:

• GitHub Issues
• GitHub Discussions
• Forum

You may reach out to project maintainers privately at [e-mail]

🛠 Installation

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

• PyPi (recommended by default)

pip install scikit-learn-intelex

• Anaconda Cloud from Conda-Forge channel (recommended for conda users by default)

conda install scikit-learn-intelex -c conda-forge

• Anaconda Cloud from Intel channel (recommended for Intel® Distribution for Python users)

conda install scikit-learn-intelex -c intel

[Click to expand] ℹ️ Supported configurations

📦 PyPi channel

OS / Python version	Python 3.6	Python 3.7	Python 3.8	Python 3.9
Linux	[CPU, GPU]	[CPU, GPU]	[CPU, GPU]	❌
Windows	[CPU, GPU]	[CPU, GPU]	[CPU, GPU]	❌
OsX	[CPU]	[CPU]	[CPU]	❌

📦 Anaconda Cloud: Conda-Forge channel

OS / Python version	Python 3.6	Python 3.7	Python 3.8	Python 3.9
Linux	[CPU]	[CPU]	[CPU]	[CPU]
Windows	[CPU]	[CPU]	[CPU]	[CPU]
OsX	[CPU]	[CPU]	[CPU]	[CPU]

📦 Anaconda Cloud: Intel channel

OS / Python version	Python 3.6	Python 3.7	Python 3.8	Python 3.9
Linux	[CPU, GPU]	[CPU, GPU]	[CPU, GPU]	❌
Windows	[CPU, GPU]	[CPU, GPU]	[CPU, GPU]	❌
OsX	[CPU]	[CPU]	[CPU]	❌

⚠️ Note: GPU support is an optional dependency. Required dependencies for GPU support will not be downloaded. You need to manually install dpcpp_cpp_rt package.

[Click to expand] ℹ️ How to install dpcpp_cpp_rt package

• PyPi

pip install --upgrade dpcpp_cpp_rt

• Anaconda Cloud

conda install dpcpp_cpp_rt -c intel

You can build the package from sources as well.

⚡️ Get Started

Intel CPU optimizations patching

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)

Intel GPU optimizations patching

import numpy as np
from sklearnex import patch_sklearn
from daal4py.oneapi import sycl_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 sycl_context("gpu"):
    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

[Click to expand] ℹ️ Reproduce results

• With Intel® Extension for Scikit-learn enabled:

python runner.py --configs configs/blogs/skl_conda_config.json –report

• With the original Scikit-learn:

python runner.py --configs configs/blogs/skl_conda_config.json –report --no-intel-optimized

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.

⚠️ We support optimizations for the last four versions of scikit-learn. The latest release of Intel(R) Extension for Scikit-learn 2021.3.X supports scikit-learn 0.22.X, 0.23.X, 0.24.X and 1.0.X.

📜 Intel(R) Extension for Scikit-learn verbose

To find out which implementation of the algorithm is currently used (Intel(R) Extension for Scikit-learn or original Scikit-learn), set the environment variable:

• On Linux and Mac OS: export SKLEARNEX_VERBOSE=INFO
• On Windows: set SKLEARNEX_VERBOSE=INFO

For example, for DBSCAN you get one of these print statements depending on which implementation is used:

• SKLEARNEX INFO: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
• SKLEARNEX INFO: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn

Read more in the documentation.