Boosting Python Performance with Intel? oneAPI Frameworks: An Overview

Python is a popular programming language for numerical and scientific computing, machine learning, and data analytics. While Python offers simplicity and ease of use, it may not always deliver the optimal performance for computationally intensive tasks. To address this issue, Intel? has developed a set of essential packages optimized for high-performance computing. In this article, we will explore the key features and benefits of Intel's oneAPI frameworks, including the Intel? Distribution for Python, Intel? Extension for Scikit-learn, Intel? Extension for PyTorch, Intel? Extension for TensorFlow, and Intel? Optimization for XGBoost.

High-Performance Python with Intel? Distribution for Python

The Intel Distribution for Python aims to achieve near-native code performance by accelerating core numerical and machine learning packages using libraries such as the Intel? oneAPI Math Kernel Library and Intel? oneAPI Data Analytics Library. By leveraging the latest CPU instructions and utilizing all available CPU cores, this distribution maximizes performance on a wide range of devices, from laptops and desktops to powerful servers. Intel Distribution for Python also provides productivity tools for compiling Python code into optimized instructions and essential Python bindings for easy integration with Intel native tools.

Scale your scikit-learn (sklearn) workflows with Intel? Extension for Scikit-learn

Scikit-learn is a widely used Python module for machine learning, and the Intel Extension for Scikit-learn seamlessly accelerates scikit-learn applications for Intel CPUs and GPUs. Key features include:

• Accelerate a wide range of sklearn algorithms, including popular ones for classification, regression, clustering, and more.
• Choose your preferred hardware for acceleration, whether it's an x86-compatible CPU or an Intel GPU, as the acceleration is compatible with both.
• Patch all compatible algorithms from the command line without requiring any code changes.
• Add just two lines of code to your Python script to patch all compatible algorithms.
• Specify in your script to patch only selected algorithms, providing granular control over acceleration.
• Globally patch and unpatch your environment, ensuring all uses of scikit-learn benefit from the acceleration.
• Integrate the accelerated versions and experience the performance gains without extensive code modifications.

Speed Up AI from Research to Production Deployment with Intel Extension for PyTorch

The Intel Extension for PyTorch maximizes PyTorch performance on Intel hardware by providing the most up-to-date Intel software and hardware optimizations. With this extension, developers can automatically mix different precision data types, reducing the model size and computational workload for inference. Key features include:

• Automatic mixing of precision data types to reduce model size and computational workload.
• Customization options through APIs for performance enhancements.
• Optimizations for Intel hardware, including Intel oneAPI Deep Neural Network Library (oneDNN), Intel Deep Learning Boost, Intel Advanced Vector Extensions (Intel AVX-512), and Intel Advanced Matrix Extensions (Intel AMX).
• Compatibility with open source PyTorch and close collaboration with the PyTorch project.
• Parallelization and distribution capabilities with oneAPI Collective Communications Library (oneCCL) bindings.
• Support for Intel GPU hardware.
• Deployment optimization with OpenVINO Toolkit for model compression and increased inference speed.
• Targeting of various Intel hardware components, including CPUs, GPUs, VPUs, and FPGAs.
• Deployment options with OpenVINO model server for optimized inference in different environments.

Production Performance for AI and Machine Learning with Intel Extension for TensorFlow

TensorFlow is a widely adopted AI and machine learning platform used for production AI development and deployment. The Intel Extension for TensorFlow provides the most up-to-date Intel software and hardware optimizations to speed up TensorFlow-based training and inference on Intel CPUs and GPUs. Key features include:

• Acceleration of AI performance using Intel oneAPI Deep Neural Network Library (oneDNN) features, including graph optimizations and memory pool allocation.
• Automatic utilization of Intel Deep Learning Boost instruction set features for parallelization and acceleration of AI workloads.
• Reduction of inference latency for models deployed with TensorFlow Serving.
• Automatic integration of oneDNN optimizations starting from TensorFlow 2.9.
• Option to enable optimizations in TensorFlow 2.5 through 2.8 by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
• Seamless integration with TensorFlow 2.10 or later for accelerated training and inference on Intel GPU hardware without requiring code changes.
• Automatic mixing of precision using bfloat16 or float16 data types to reduce memory usage and improve performance.
• Utilization of TensorFloat-32 (TF32) math mode on Intel GPU hardware.
• Optimization of CPU performance settings for latency or throughput using an autotuned CPU launcher.
• More aggressive fusion through the oneDNN Graph API.
• Import TensorFlow models into OpenVINO? Runtime and utilize the Neural Networks Compression Framework (NNCF) to compress model size and enhance inference speed.
• Deployment with the OpenVINO model server for optimized inference, accessible through the same API as TensorFlow Serving.
• Targeting a combination of Intel CPUs, GPUs (integrated or discrete), VPUs, or FPGAs.
• Deployment options include on-premise, on-device, in the browser, or in the cloud.

Fast Turnaround for Gradient Boosting Machine Learning with Intel? Optimization for XGBoost

XGBoost is a gradient boosting machine learning library known for its performance across a variety of data and problem types. Intel Optimization for XGBoost allows developers to automatically accelerate XGBoost training and inference on Intel CPUs without requiring any code changes. Gradient Boosting Algorithms: Implement classification, regression, and ranking machine learning algorithms using gradient boosting techniques.Key features include:

• Perform parallel tree boosting, enabling efficient and accurate solutions for a wide range of machine learning problems.
• Run training processes on a single node or distribute them across multiple nodes for faster and scalable training.
• Improve the speed of XGBoost histogram tree-building through automatic memory prefetching.
• Parallelize the XGBoost split function by automatically partitioning observations into multiple processing threads, enhancing performance.
• Optimize memory usage during histogram building, resulting in reduced memory consumption.
• Utilize daal4py, leveraging Intel's oneAPI Data Analytics Library (oneDAL) optimizations, to further accelerate XGBoost model inference. These optimizations may not yet be ported to the XGBoost library.
• Easily import pretrained or custom-trained XGBoost and LightGBM models using a few lines of code in daal4py.
• Reduce memory consumption during inference and utilize L1 and L2 caches more efficiently, improving performance.

The Intel oneAPI frameworks provide powerful tools for achieving high-performance Python computing. Whether you are a beginner, a researcher, a scientific computing developer, an HPC developer, or a machine learning practitioner, these frameworks offer accelerated performance, optimized algorithms, and advanced features to help you unlock the full potential of your Python applications. By leveraging Intel's hardware and software optimizations, you can accelerate your workflows, scale your applications, and deliver faster results in a wide range of domains.