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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.

Speed up model development and deployment performance on Intel hardware with software optimizations built into open source PyTorch.

With a few lines of code, Intel® Extension for PyTorch* enables the most up-to-date Intel software and hardware optimizations for AI.

Using this framework with Intel optimizations, you can:

  • Develop, train, and deploy AI models using a Python* or C++ API.
  • Automatically accelerate PyTorch-based training and inference performance on Intel hardware.
  • Extend PyTorch to further accelerate performance on Intel hardware with minimal code changes.
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 Free Intel® Cloud

Get what you need to build and optimize your oneAPI projects for free. With an Intel® Developer Cloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel® oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.

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

PyTorch Machine Learning Framework

  • Create, train, and deploy deep learning models using a Python or C++ API.
  • Transition from interactive development in eager mode to fast batch runtimes with graph mode.
  • Speed up model development with built-in support for distributed training on a variety of platforms.
  • Deploy PyTorch models to production servers with TorchServe.

 

Intel® Optimizations

  • Accelerate PyTorch model performance with Intel® oneAPI Deep Neural Network Library features such as graph and node optimizations.
  • Automatically use Intel® Deep Learning Boost instruction set features to parallelize and accelerate PyTorch workloads.
  • Reduce inference latency for models deployed with TorchServe.
  • Perform distributed training with oneAPI Collective Communications Library Bindings for Pytorch*.

Intel® Extension for PyTorch* Optimizations and Features

  • Apply the newest performance optimizations not yet in PyTorch using Python API commands.
  • Vectorize operations to take advantage of larger register sizes in Intel® Advanced Vector Extensions 2, Intel® Advanced Vector Extensions 512, and Intel® Advanced Matrix Extensions instruction sets.
  • Parallelize operations without having to analyze task dependencies.
  • Further improve vectorization by quantizing to smaller word lengths such as bfloat16 (BF16) or INT8.
  • Use built-in recipes to balance quantization efficiency with minimal accuracy loss.
  • Fuse common FP32 and BF16 graph operations such as Conv2D+ReLU or Linear+ReLU.
  • Fold mathematical graph operations with a convolution.
  • Control aspects of the thread runtime such as multistream inference and asynchronous task spawning.


Benchmarks

Documentation & Code Samples

Documentation

  • PyTorch Documentation
  • PyTorch Performance Tuning Guide
  • Intel Extension for PyTorch
    • GitHub Documentation & Tutorials
    • Installation Guide (All Operating Systems)
    • API Documentation
    • Release Notes
    • System Requirements
    • TorchServe with Intel Extension for PyTorch
  • Intel® oneAPI Collective Communications Library (oneCCL) Bindings for PyTorch

 

View All Documentation

Code Samples

  • Visual Quality Inspection for the Pharmaceutical Industry
  • Single-Instance Training
  • bfloat16 Inference—Imperative Mode
  • bfloat16 Inference—TorchScript Mode
  • INT8 Deployment—Graph Mode
  • C++ Dynamic Library

 

More Samples

Training

Get Started with the Intel Extension for PyTorch

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

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

 

Accelerate bfloat16 PyTorch Models

Get an introduction to Intel Extension for PyTorch and Intel® oneAPI Deep Neural Network Library (oneDNN) with a close look into the technology behind the Intel Extension for PyTorch API and graph fusion optimizations.

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

 

Intel and Facebook Collaborate to Boost PyTorch CPU Performance

Learn how Intel software optimizations accelerate PyTorch on Intel CPU hardware.

Learn More

Specifications

Processor:

  • Intel Xeon Scalable processor

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

Sign up to receive the latest trends, tutorials, tools, training, and more to
help you write better code optimized for CPUs, GPUs, FPGAs, and other
accelerators—stand-alone or in any combination.

 

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