Optimized Deep Learning 

• Leverage popular, Intel-optimized frameworks—including TensorFlow and PyTorch—to use the full power of Intel® architecture and yield high performance for training and inference.
• Expedite development by using the open source, pretrained, machine learning models that are optimized by Intel for best performance. 
• Take advantage of automatic accuracy-driven tuning strategies along with additional objectives like performance, model size, or memory footprint using low-precision optimizations.
   

Data Analytics and Machine Learning Acceleration

• Increase machine learning model accuracy and performance with algorithms in scikit-learn and XGBoost, optimized for Intel architecture.
• Scale out efficiently to clusters and perform distributed machine learning by using Intel Extension for Scikit-learn.

High-Performance Python*

• Take advantage of the most popular and fastest growing programming language for AI and data analytics with underlying instruction sets optimized for Intel architecture.
• Process larger scientific data sets more quickly using drop-in performance enhancements to existing Python code.
• Achieve highly efficient multithreading, vectorization, and memory management, and scale scientific computations efficiently across a cluster.

Simplified Scaling across Multi-node DataFrames

• Seamlessly scale and accelerate pandas workflows to multicores and multi-nodes with only one line of code change using the Modin, an extremely lightweight parallel DataFrame.
• Accelerate data analytics with high-performance compute engines, such as Ray, Dask*, or Message Passing Interface (MPI).

PyTorch* Optimizations from Intel

Intel is one of the largest contributors to PyTorch, providing regular upstream optimizations to the PyTorch deep learning framework that provide superior performance on Intel architectures. The AI Tools Selector includes the latest binary version of PyTorch tested to work with the rest of the tools, along with Intel Extension for PyTorch, which adds the newest Intel optimizations and usability features.

TensorFlow* Optimizations from Intel

TensorFlow has been directly optimized for Intel architecture, in collaboration with Google*, using the primitives of Intel® oneAPI Deep Neural Network Library (oneDNN) to maximize performance. The AI Tools Selector provides the latest binary version compiled with CPU-enabled settings, along with Intel Extension for TensorFlow, which seamlessly plugs into the stock version to add support for new devices and optimizations.

Intel® Neural Compressor

Reduce model size and speed up inference for deployment on CPUs or GPUs. The open source library provides a framework-independent API to perform model compression techniques such as quantization, pruning, and knowledge distillation.

Intel® Tiber™ AI Studio

Get repeatable value with a simpler way to build, deploy, and manage models on any infrastructure. Intel Tiber AI Studio is a full-service machine learning operating system that enables you to manage all your AI projects from one place. It is an optional component and requires a separate license.

Intel® Extension for Scikit-learn*

Seamlessly speed up your scikit-learn applications on Intel® CPUs and GPUs across single nodes and multi-nodes. This extension package dynamically patches scikit-learn estimators to use oneDAL as the underlying solver, while achieving the speed up for your machine learning algorithms. The AI Tools Selector also includes stock scikit-learn to provide a comprehensive Python environment installed with all required packages. The extension supports up to the last four versions of scikit-learn, and provides flexibility to use with your existing packages.  

Intel® Optimization for XGBoost*

In collaboration with the XGBoost community, Intel has been directly upstreaming many optimizations to provide superior performance on Intel CPUs. This well-known machine learning package for gradient-boosted decision trees now includes seamless, drop-in acceleration for Intel architecture to significantly speed up model training and improve accuracy for better predictions.
 

Modin*

Accelerate your pandas workflows and scale data preprocessing across multi-nodes using this intelligent, distributed DataFrame library with an identical API to pandas. The library integrates with Ray, Dask*, and MPI compute engines to distribute the data without having to write code.

OpenVINO^TM Toolkit

Convert and optimize models trained using popular frameworks like TensorFlow and PyTorch. Optimize and deploy with best-in-class performance across a mix of Intel CPUs, GPUs (integrated or discrete), NPUs, or FPGAs.

Intel® Gaudi® Software

Efficiently map models developed using PyTorch and TensorFlow onto Intel Gaudi AI accelerators. The software suite includes a graph compiler and runtime, a Tensor Processor Core (TPC)* kernel library, firmware and drivers, and developer tools for custom kernel development and profiling.

• AI Tools Selector Guide
• Get Started Guide
• Release Notes
• AI Performance Debugging on Intel CPUs

View All Documentation

• Get Started:
  TensorFlow | PyTorch | Modin | XGBoost | scikit-learn
  
• End-to-End Machine Learning for Census Workload
• TensorFlow Performance Analysis
• Multi-node Training with PyTorch
• PyTorch Training with Intel® Advanced Matrix Extensions and bfloat16 Data
• Intel AI Reference Models

oneAPI Code Sample Catalog