As of now, Intel(R) has not released an official extension for Scikit-learn in Python. However, there are ways you can optimize and speed up your Scikit-learn code using Intel’s tools and libraries. In this tutorial, we will explore some of the best practices for optimizing Scikit-learn code using Intel(R) tools and libraries.
1. Use Intel(R) Distribution for Python:
Intel(R) Distribution for Python is a high-performance distribution of Python that comes with optimized libraries such as NumPy and SciPy. By using Intel(R) Distribution for Python, you can take advantage of Intel’s optimized libraries to speed up your Scikit-learn code.
To install Intel(R) Distribution for Python, you can download the distribution from the Intel website and follow the installation instructions. Once installed, you can use Intel(R) Distribution for Python as your default Python interpreter.
2. Enable Intel(R) Performance Libraries:
Intel provides optimized libraries such as Intel(R) Math Kernel Library (MKL) and Intel(R) Data Analytics Acceleration Library (DAAL) that can significantly speed up mathematical computations in Scikit-learn.
To enable Intel(R) Performance Libraries in your Scikit-learn code, you can set the environment variable “OMP_NUM_THREADS” to control the number of threads used for parallel processing. For example, you can set “OMP_NUM_THREADS=1” to use a single thread for computations.
You can also link your Scikit-learn code with Intel’s MKL libraries to take advantage of optimized linear algebra functions. To do this, you can install the MKL libraries and link them with your code using the appropriate compiler flags.
3. Use Intel(R) VTune(TM) Profiler for Performance Tuning:
Intel(R) VTune(TM) Profiler is a powerful performance profiling tool that can help you identify performance bottlenecks in your Scikit-learn code. By profiling your code with Intel(R) VTune(TM) Profiler, you can optimize the hotspots and improve the overall performance of your code.
To use Intel(R) VTune(TM) Profiler, you can install the tool and follow the instructions to set up profiling for your Scikit-learn code. Once configured, you can run your code under the profiler and analyze the performance metrics to identify areas for optimization.
4. Use Intel(R) Distribution for Machine Learning Toolkit (DMLT) with Scikit-learn:
Intel(R) Distribution for Machine Learning Toolkit (DMLT) is a set of libraries and tools that can be used alongside Scikit-learn to accelerate machine learning workflows. DMLT includes optimized versions of popular machine learning algorithms and tools for distributed computing.
To use Intel(R) DMLT with Scikit-learn, you can install the toolkit and use the provided APIs to accelerate your machine learning workflows. By leveraging Intel(R) DMLT, you can speed up training and inference tasks in Scikit-learn and improve the overall performance of your machine learning models.
In conclusion, while there is no official Intel(R) extension for Scikit-learn in Python, you can still optimize your Scikit-learn code using Intel’s tools and libraries. By using Intel(R) Distribution for Python, enabling Intel(R) Performance Libraries, using Intel(R) VTune(TM) Profiler for performance tuning, and leveraging Intel(R) DMLT, you can enhance the performance of your Scikit-learn code and accelerate your machine learning workflows.
