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Intel® Optimization for PyTorch*

Speed Up AI from Research to Production Deployment

  

Maximize PyTorch Performance on Intel Hardware

PyTorch* is an AI and machine learning framework popular for both research and production usage. This open source library is often used for deep learning applications whose compute-intensive training and inference test the limits of available hardware resources.

Intel releases its newest optimizations and features in Intel® Extension for PyTorch* before upstreaming them into open source PyTorch.

With a few lines of code, you can use Intel Extension for PyTorch to:

  • Take advantage of the most up-to-date Intel software and hardware optimizations for PyTorch.
  • Automatically mix different precision data types to reduce the model size and computational workload for inference.
  • Add your own performance customizations using APIs.

Intel also works closely with the open source PyTorch project to optimize the PyTorch framework for Intel hardware. All of these optimizations are collectively referred to as Intel Optimization for PyTorch.

Intel Optimization for PyTorch is part of the end-to-end suite of Intel® AI and machine learning development tools and resources.

Download as Part of the Toolkit

PyTorch and Intel Extension for PyTorch are available in the Intel® AI Analytics Toolkit, which provides accelerated machine learning and data analytics pipelines with optimized deep learning frameworks and high-performing Python libraries.

Get It Now
Develop in the Cloud

Build and optimize oneAPI multiarchitecture applications using the latest optimized Intel® oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary. Free for 120 days with extensions possible.

Get Access
Download the Stand-Alone Versions

Stand-alone versions of PyTorch and Intel Extension for PyTorch are available. You can install them using a package manager or build from the source.

 

PyTorch | Intel Extension for PyTorch

Help Intel® Extension for PyTorch* Evolve

This open source component has an active developer community. We welcome you to participate.

Open Source Version (GitHub*)



Features

Open Source PyTorch Powered by Intel® Optimization

Accelerate PyTorch training and inference with Intel® oneAPI Deep Neural Network Library (oneDNN) features such as graph and node optimizations.

  • Take advantage of Intel® Deep Learning Boost, Intel® Advanced Vector Extensions (Intel® AVX-512), and Intel® Advanced Matrix Extensions (Intel® AMX) instruction set features to parallelize and accelerate PyTorch workloads.
  • Speed up turnaround time on Intel hardware from interactive development to batch training and inference.
  • Perform distributed training with oneAPI Collective Communications Library (oneCCL) bindings for PyTorch.
  • Reduce inference latency for models deployed to production servers with TorchServe.

Intel Extension for PyTorch Optimizations and Features

  • Apply the newest performance optimizations not yet in PyTorch using Python API commands.
  • Parallelize operations without having to analyze task dependencies.
  • Automatically mix operator data type precision between float32 and bfloat16 to reduce computational workload and model size.
  • Fuse and optimize frequently used convolution operations.
  • Convert to a channels-last memory format for faster image-based deep learning performance.
  • Control aspects of the thread runtime such as multistream inference and asynchronous task spawning.
  • Run PyTorch on Intel GPU hardware.


Benchmarks

Documentation & Code Samples

Documentation

  • PyTorch Documentation
  • PyTorch Performance Tuning Guide
  • Intel Extension for PyTorch
    • Documentation & Tutorials
    • Installation Guide (All Operating Systems)
    • Get Started: Cheat Sheet
    • Performance Tuning Guide
    • Release Notes
    • System Requirements
    • GPU Release Documentation and Installation
  • TorchServe with Intel Extension for PyTorch
  • oneCCL Bindings for PyTorch
 

View All Documentation

Intel Extension for PyTorch Code Samples

  • Single-Instance Training
  • bfloat16 Inference—Imperative Mode
  • bfloat16 Inference—TorchScript Mode
  • int8 Deployment—Graph Mode
  • C++ Dynamic Library
  • GPU Single-Instance Training
  • GPU Inference
 

More Samples

Training & Tutorials

Visual Quality Inspection for the Pharmaceutical Industry

Get Started with Intel Extension for PyTorch

Optimize PyTorch* Performance on the Latest Intel® CPUs and GPUs

Hands-On Workshop: Accelerate PyTorch Applications Using Intel® oneAPI Toolkit

Optimize the Latest Deep Learning Workloads Using Intel Optimization for PyTorch

How to Improve TorchServe Inference Performance with Intel Extension for PyTorch

Demonstrations

Achieve Up to 1.77x Boost Ratio for Your AI Workloads

Learn the difference between stock PyTorch and the Intel Extension for PyTorch, followed by in-depth explanations of the key techniques that power this extension.

Watch

 

Increase PyTorch Inference Throughput by 4x

See how to accelerate PyTorch-based inferencing by applying optimizations from the Intel Extension for PyTorch and quantizing to int8.

Watch

Accelerate MedMNIST Training and Inference with Intel Extension for PyTorch

See how to use Intel Extension for PyTorch for training and inference on the MedMNIST datasets. It is compared against stock PyTorch and shows the performance gain that Intel Extension for PyTorch offers.

Watch

 

Speed Training 8x Using PyTorch with a oneCCL Back End

Compare the performance of distributed training of the deep learning recommendation model (DLRM) using oneCCL and other leading back-ends.

Read

Case Studies

AI-Based Customer Service Automation—Conversations in the Cloud

MindTitan* and Intel worked together to optimize their TitanCS solution using Intel Extension for PyTorch, achieving improvements on inference performance running on Intel CPUs and driving better real-time call analysis.

Listen

KT Optimizes Performance for Personalized Text-to-Speech

Technologists from KT (formerly Korea Telecom) and Intel worked together to optimize performance of the company’s P-TTS service. The optimized CPU-based solution increased real-time function (RTF) performance by 22 percent while maintaining voice quality and number of connections.

Learn More

News

Introducing Intel Extension for PyTorch for GPUs

This extension now supports Intel GPUs. Learn which features are supported in this release, how to install it, and how to get started running PyTorch on Intel GPUs.

Learn More

 

Empower PyTorch on Intel® Xeon® Scalable processors with bfloat16

Intel and Meta continue to collaborate to improve PyTorch bfloat16 performance by taking advantage of Intel AVX-512 and Intel AMX instruction set extensions.

Learn More

 

What Is New in Intel Extension for PyTorch

This presentation from the PyTorch Conference 2022 provides insight into the software optimizations and features (such as GPU support) that are introduced in Intel Extension for PyTorch and upstreamed to open source PyTorch over time.

Learn More

PyTorch v1.13: A New Potential to Enhance Model Performance and Accuracy

Monitor and improve application performance with new Intel Optimizations and features in the open source framework and in Intel Extension for PyTorch.

Learn More

 

Accelerate Deep Learning with Intel Extension for PyTorch

See how to use Intel Extension for PyTorch to take advantage of optimizations before they become part of a stock PyTorch release. Apply the newest developments to optimize your PyTorch models running on Intel hardware.

Learn More

 

Intel and Facebook* Accelerate PyTorch Performance

Facebook* and Intel collaborated to improve PyTorch performance on 3rd generation Intel® Xeon® Scalable processors by harnessing the new bfloat16 capability in Intel® Deep Learning Boost, and deliver training and inference performance boosts for a variety of model and data types.

Learn More

Specifications

Processors:

  • Intel Xeon processor
  • Intel® Core™ processor
  • Intel® Data Center GPU Flex Series


Operating systems:

  • Linux* (Intel Extension for PyTorch is for Linux only)
  • Windows*

Languages:

  • Python
  • C++

Get Help

Your success is our success. Access these support resources when you need assistance.

  • Intel AI Analytics Toolkit Support Forum
  • Intel® Optimized AI Frameworks Support Forum
  • Intel Extension for PyTorch GitHub Issue Tickets

Stay in the Know with All Things CODE

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