Boost Scikit-learn Performance with Just Two Lines of Code | Intel Software

Scikit-learn is one of the most widely used machine learning libraries in Python due to its ease of use, flexibility, and robustness. However, when working with large datasets or complex models, performance can become a bottleneck. This is where Intel’s oneAPI comes in, offering acceleration capabilities for scikit-learn models with just two lines of code.

In this tutorial, we will walk through the process of accelerating scikit-learn with Intel’s oneAPI using the daal4py library. Daal4py provides drop-in replacements for popular scikit-learn algorithms that leverage Intel’s oneAPI for enhanced performance.

Please note that to follow this tutorial, you will need access to an Intel CPU with Advanced Vector Extensions (AVX) support and have the Intel oneAPI base kit installed. You can download the oneAPI base kit from the Intel website.

Step 1: Install daal4py

The first step is to install the daal4py library. You can do this using pip:

pip install daal4py

Step 2: Accelerate scikit-learn with daal4py

Now that you have daal4py installed, you can easily accelerate your scikit-learn models with just two lines of code. Simply import the daal4py library and set the environment variable to ensure that daal4py is used:

import daal4py.sklearn
import os
os.environ["SKLEARNEX_PYDAAL_ENGINE"] = "daal"

With these two lines of code, daal4py will be used as the backend for scikit-learn algorithms, providing accelerated performance on Intel CPUs with AVX support.

Step 3: Use accelerated scikit-learn algorithms

Once you have set up daal4py as the backend for scikit-learn, you can use any scikit-learn algorithm as you normally would. For example, you can train a linear regression model on a dataset using daal4py:

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# Create a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

In this example, the linear regression model is trained on the Boston housing dataset and used to make predictions. The daal4py library will accelerate the training process, resulting in faster execution times compared to the standard scikit-learn implementation.

Conclusion

In this tutorial, we have demonstrated how to accelerate scikit-learn models with Intel’s oneAPI using the daal4py library. By simply importing daal4py and setting the environment variable, you can leverage the power of Intel CPUs with AVX support to enhance the performance of your machine learning models. Give it a try and see the difference in execution times for yourself!