# Intel(R) Extension for Scikit-learn*
[](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=master)
[](https://scan.coverity.com/projects/daal4py)
[](https://github.com/intel/scikit-learn-intelex/discussions)
[](https://pypi.org/project/scikit-learn-intelex/)
[](https://anaconda.org/conda-forge/scikit-learn-intelex)
With Intel(R) Extension for Scikit-learn you can accelerate your Scikit-learn applications and still have full conformance with all Scikit-Learn APIs and algorithms. This is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications. And you do not even need to change the existing code!
The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library ([oneDAL](https://github.com/oneapi-src/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**](https://github.com/intel/scikit-learn-intelex/tree/master/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](https://intelpython.github.io/daal4py).
## 👀 Follow us on Medium
We publish blogs on Medium, so [follow us](https://medium.com/intel-analytics-software/tagged/machine-learning) 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](https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4)
- [Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors](https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3)
- [Leverage Intel Optimizations in Scikit-Learn](https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544)
- [Intel Gives Scikit-Learn the Performance Boost Data Scientists Need](https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18)
- [From Hours to Minutes: 600x Faster SVM](https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae)
- [Improve the Performance of XGBoost and LightGBM Inference](https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e)
- [Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit](https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a)
- [Accelerate Your scikit-learn Applications](https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e)
- [Accelerate Linear Models for Machine Learning](https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe)
- [Accelerate K-Means Clustering](https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1)
## 🔗 Important links
- [Notebook examples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks)
- [Documentation](https://intel.github.io/scikit-learn-intelex/)
- [scikit-learn API and patching](https://intel.github.io/scikit-learn-intelex/)
- [Benchmark code](https://github.com/IntelPython/scikit-learn_bench)
- [Building from Sources](https://github.com/intel/scikit-learn-intelex/blob/master/INSTALL.md)
- [About Intel(R) oneAPI Data Analytics Library](https://github.com/oneapi-src/oneDAL)
- [About Intel(R) daal4py](https://github.com/intel/scikit-learn-intelex/tree/master/daal4py)
## 💬 Support
Report issues, ask questions, and provide suggestions using:
- [GitHub Issues](https://github.com/intel/scikit-learn-intelex/issues)
- [GitHub Discussions](https://github.com/intel/scikit-learn-intelex/discussions)
- [Forum](https://community.intel.com/t5/Intel-Distribution-for-Python/bd-p/distribution-python)
You may reach out to project maintainers privately at onedal.maintainers@intel.com
# 🛠 Installation
Intel(R) Extension for Scikit-learn is available at the [Python Package Index](https://pypi.org/project/scikit-learn-intelex/),
on Anaconda Cloud in [Conda-Forge channel](https://anaconda.org/conda-forge/scikit-learn-intelex) and in [Intel channel](https://anaconda.org/intel/scikit-learn-intelex).
Intel(R) Extension for Scikit-learn is also available as a part of [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html) (AI Kit).
- PyPi (recommended by default)
```bash
pip install scikit-learn-intelex
```
- Anaconda Cloud from Conda-Forge channel (recommended for conda users by default)
```bash
conda config --add channels conda-forge
conda config --set channel_priority strict
conda install scikit-learn-intelex
```
- Anaconda Cloud from Intel channel (recommended for Intel® Distribution for Python users)
```bash
conda config --add channels intel
conda config --set channel_priority strict
conda install scikit-learn-intelex
```
<details><summary>[Click to expand] ℹ️ Supported configurations </summary>
#### 📦 PyPi channel
| OS / Python version | **Python 3.8** | **Python 3.9** | **Python 3.10**| **Python 3.11**| **Python 3.12**|
| :-----------------------| :------------: | :-------------:| :------------: | :------------: | :------------: |
| **Linux** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |
| **Windows** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |
#### 📦 Anaconda Cloud: Conda-Forge channel
| OS / Python version | **Python 3.8** | **Python 3.9** | **Python 3.10**| **Python 3.11**| **Python 3.12**|
| :-----------------------| :------------: | :------------: | :------------: | :------------: | :------------: |
| **Linux** | [CPU] | [CPU] | [CPU] | [CPU] | [CPU] |
| **Windows** | [CPU] | [CPU] | [CPU] | [CPU] | [CPU] |
#### 📦 Anaconda Cloud: Intel channel
| OS / Python version | **Python 3.8** | **Python 3.9** | **Python 3.10**| **Python 3.11**| **Python 3.12**|
| :-----------------------| :------------: | :-------------:| :------------: | :------------: | :------------: |
| **Linux** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |
| **Windows** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |
</details>
⚠️ 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.*
<details><summary>[Click to expand] ℹ️ How to install dpcpp_cpp_rt package </summary>
- PyPi
```bash
pip install --upgrade dpcpp_cpp_rt
```
- Anaconda Cloud
```bash
conda install dpcpp_cpp_rt -c intel
```
</details>
You can [build the package from sources](https://github.com/intel/scikit-learn-intelex/blob/master/INSTALL.md) as well.
# ⚡️ Get Started
Intel CPU optimizations patching
```py
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
```py
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)
```
# 🚀 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](https://github.com/IntelPython/scikit-learn_bench)
<details><summary>[Click to expand] ℹ️ Reproduce results </summary>
- With Intel® Extension for Scikit-learn enabled:
```bash
python runner.py --configs configs/blogs/skl_conda_config.json -–report
```
- With the original Scikit-learn:
```bash
python runner.py --configs configs/blogs/skl_conda_config.json -–report --no-intel-optimized
```
</details>
Intel(R) Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) 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](https://github.com/intel/scikit-learn-intelex/issues).
⚠️ We support optimizations for the last four versions of scikit-learn. The latest release of scikit-learn-intelex-2024.0.X supports scikit-learn 1.0.X, 1.1.X, 1.2.X and 1.3.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: `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](https://intel.github.io/scikit-learn-intelex/).
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"description": "\n# Intel(R) Extension for Scikit-learn*\n\n[](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=master)\n[](https://scan.coverity.com/projects/daal4py)\n[](https://github.com/intel/scikit-learn-intelex/discussions)\n[](https://pypi.org/project/scikit-learn-intelex/)\n[](https://anaconda.org/conda-forge/scikit-learn-intelex)\n\nWith Intel(R) Extension for Scikit-learn you can accelerate your Scikit-learn applications and still have full conformance with all Scikit-Learn APIs and algorithms. This is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications. And you do not even need to change the existing code!\n\nThe acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library ([oneDAL](https://github.com/oneapi-src/oneDAL)). Patching scikit-learn makes it a well-suited machine learning framework for dealing with real-life problems.\n\n\u26a0\ufe0fIntel(R) Extension for Scikit-learn contains scikit-learn patching functionality that was originally available in [**daal4py**](https://github.com/intel/scikit-learn-intelex/tree/master/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.\nYou can learn more about daal4py in [daal4py documentation](https://intelpython.github.io/daal4py).\n\n## \ud83d\udc40 Follow us on Medium\n\nWe publish blogs on Medium, so [follow us](https://medium.com/intel-analytics-software/tagged/machine-learning) 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:\n\n- [Save Time and Money with Intel Extension for Scikit-learn](https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4)\n- [Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors](https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3)\n- [Leverage Intel Optimizations in Scikit-Learn](https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544)\n- [Intel Gives Scikit-Learn the Performance Boost Data Scientists Need](https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18)\n- [From Hours to Minutes: 600x Faster SVM](https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae)\n- [Improve the Performance of XGBoost and LightGBM Inference](https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e)\n- [Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit](https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a)\n- [Accelerate Your scikit-learn Applications](https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e)\n- [Accelerate Linear Models for Machine Learning](https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe)\n- [Accelerate K-Means Clustering](https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1)\n\n## \ud83d\udd17 Important links\n- [Notebook examples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks)\n- [Documentation](https://intel.github.io/scikit-learn-intelex/)\n- [scikit-learn API and patching](https://intel.github.io/scikit-learn-intelex/)\n- [Benchmark code](https://github.com/IntelPython/scikit-learn_bench)\n- [Building from Sources](https://github.com/intel/scikit-learn-intelex/blob/master/INSTALL.md)\n- [About Intel(R) oneAPI Data Analytics Library](https://github.com/oneapi-src/oneDAL)\n- [About Intel(R) daal4py](https://github.com/intel/scikit-learn-intelex/tree/master/daal4py)\n\n## \ud83d\udcac Support\n\nReport issues, ask questions, and provide suggestions using:\n\n- [GitHub Issues](https://github.com/intel/scikit-learn-intelex/issues)\n- [GitHub Discussions](https://github.com/intel/scikit-learn-intelex/discussions)\n- [Forum](https://community.intel.com/t5/Intel-Distribution-for-Python/bd-p/distribution-python)\n\nYou may reach out to project maintainers privately at onedal.maintainers@intel.com\n\n# \ud83d\udee0 Installation\nIntel(R) Extension for Scikit-learn is available at the [Python Package Index](https://pypi.org/project/scikit-learn-intelex/),\non Anaconda Cloud in [Conda-Forge channel](https://anaconda.org/conda-forge/scikit-learn-intelex) and in [Intel channel](https://anaconda.org/intel/scikit-learn-intelex).\nIntel(R) Extension for Scikit-learn is also available as a part of [Intel\u00ae oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html)\u202f(AI Kit).\n\n- PyPi (recommended by default)\n\n```bash\npip install scikit-learn-intelex\n```\n\n- Anaconda Cloud from Conda-Forge channel (recommended for conda users by default)\n\n```bash\n conda config --add channels conda-forge\n conda config --set channel_priority strict\n conda install scikit-learn-intelex\n```\n\n- Anaconda Cloud from Intel channel (recommended for Intel\u00ae Distribution for Python users)\n\n```bash\n conda config --add channels intel\n conda config --set channel_priority strict\n conda install scikit-learn-intelex\n```\n\n<details><summary>[Click to expand] \u2139\ufe0f Supported configurations </summary>\n\n#### \ud83d\udce6 PyPi channel\n\n| OS / Python version | **Python 3.8** | **Python 3.9** | **Python 3.10**| **Python 3.11**| **Python 3.12**|\n| :-----------------------| :------------: | :-------------:| :------------: | :------------: | :------------: |\n| **Linux** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |\n| **Windows** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |\n\n#### \ud83d\udce6 Anaconda Cloud: Conda-Forge channel\n\n| OS / Python version | **Python 3.8** | **Python 3.9** | **Python 3.10**| **Python 3.11**| **Python 3.12**|\n| :-----------------------| :------------: | :------------: | :------------: | :------------: | :------------: |\n| **Linux** | [CPU] | [CPU] | [CPU] | [CPU] | [CPU] |\n| **Windows** | [CPU] | [CPU] | [CPU] | [CPU] | [CPU] |\n\n#### \ud83d\udce6 Anaconda Cloud: Intel channel\n\n| OS / Python version | **Python 3.8** | **Python 3.9** | **Python 3.10**| **Python 3.11**| **Python 3.12**|\n| :-----------------------| :------------: | :-------------:| :------------: | :------------: | :------------: |\n| **Linux** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |\n| **Windows** | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] | [CPU, GPU] |\n\n</details>\n\n\u26a0\ufe0f Note: *GPU support is an optional dependency. Required dependencies for GPU support\nwill not be downloaded. You need to manually install ***dpcpp_cpp_rt*** package.*\n\n<details><summary>[Click to expand] \u2139\ufe0f How to install dpcpp_cpp_rt package </summary>\n\n- PyPi\n\n```bash\npip install --upgrade dpcpp_cpp_rt\n```\n\n- Anaconda Cloud\n\n```bash\nconda install dpcpp_cpp_rt -c intel\n```\n\n</details>\n\nYou can [build the package from sources](https://github.com/intel/scikit-learn-intelex/blob/master/INSTALL.md) as well.\n\n# \u26a1\ufe0f Get Started\n\nIntel CPU optimizations patching\n```py\nimport numpy as np\nfrom sklearnex import patch_sklearn\npatch_sklearn()\n\nfrom sklearn.cluster import DBSCAN\n\nX = np.array([[1., 2.], [2., 2.], [2., 3.],\n [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)\nclustering = DBSCAN(eps=3, min_samples=2).fit(X)\n```\n\nIntel GPU optimizations patching\n```py\nimport numpy as np\nimport dpctl\nfrom sklearnex import patch_sklearn, config_context\npatch_sklearn()\n\nfrom sklearn.cluster import DBSCAN\n\nX = np.array([[1., 2.], [2., 2.], [2., 3.],\n [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)\nwith config_context(target_offload=\"gpu:0\"):\n clustering = DBSCAN(eps=3, min_samples=2).fit(X)\n```\n\n# \ud83d\ude80 Scikit-learn patching\n\n\nConfigurations:\n- HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket\n- SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8\n\n[Benchmarks code](https://github.com/IntelPython/scikit-learn_bench)\n\n<details><summary>[Click to expand] \u2139\ufe0f Reproduce results </summary>\n\n- With Intel\u00ae Extension for Scikit-learn enabled:\n\n```bash\npython runner.py --configs configs/blogs/skl_conda_config.json -\u2013report\n```\n\n- With the original Scikit-learn:\n\n```bash\npython runner.py --configs configs/blogs/skl_conda_config.json -\u2013report --no-intel-optimized\n```\n</details>\n\nIntel(R) Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) 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](https://github.com/intel/scikit-learn-intelex/issues).\n\n\u26a0\ufe0f We support optimizations for the last four versions of scikit-learn. The latest release of scikit-learn-intelex-2024.0.X supports scikit-learn 1.0.X, 1.1.X, 1.2.X and 1.3.X.\n\n## \ud83d\udcdc Intel(R) Extension for Scikit-learn verbose\n\nTo find out which implementation of the algorithm is currently used (Intel(R) Extension for Scikit-learn or original Scikit-learn), set the environment variable:\n- On Linux: `export SKLEARNEX_VERBOSE=INFO`\n- On Windows: `set SKLEARNEX_VERBOSE=INFO`\n\nFor example, for DBSCAN you get one of these print statements depending on which implementation is used:\n- `SKLEARNEX INFO: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU`\n- `SKLEARNEX INFO: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn`\n\n[Read more in the documentation](https://intel.github.io/scikit-learn-intelex/).\n\n\n",
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