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Enhance Deep Learning Workloads on the Latest Intel® Xeon® Processors

Enhance Deep Learning Workloads on the Latest Intel® Xeon® Processors

@IntelDevTools

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Overview

The 4th generation Intel® Xeon® Scalable processors (formerly code named Sapphire Rapids) offer several built-in features for boosting performance and efficiency of deep learning applications.

This session focuses on one of them—Intel® Advanced Matrix Extensions (Intel® AMX)—and how to take advantage of its AI acceleration power to boost model training and inference using Intel optimizations for PyTorch* and TensorFlow*.

Topics covered include:

  • An overview of the Intel optimizations, including performance and features on the latest Intel CPUs and how they compare to stock PyTorch and TensorFlow.
  • How the optimizations reduce a memory footprint and improve performance by automatically mixing precision using bfloat16 or float16 data types.
  • Using Intel® oneAPI Deep Neural Network Library (oneDNN) with Intel optimizations for PyTorch and TensorFlow to take advantage of other 4th gen Intel Xeon processor built-in acceleration features, such as Intel® Advanced Vector Extensions 512 and Vector Neural Network Instructions (VNNI)
  • Reducing model inference time with quantization features in Intel® Optimization for PyTorch*
  • How speedups can be gained over stock PyTorch and TensorFlow on new Amazon Web Services* instances built on Intel Xeon Scalable processors.

Skill level: Novice

 

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Featured Software

 

Featured Software

  • The Intel optimizations are available as part of the AI Tools or you can download stand-alone versions: PyTorch Optimization | TensorFlow Optimization.
  • Get the stand-alone version of oneDNN or as part of the Intel® oneAPI Base Toolkit.

 

Code Samples

Download a variety of samples on GitHub*, including:

  • Get Started with Intel® Extension for PyTorch*
  • Optimize PyTorch Models Using Quantization
  • PyTorch Training Optimizations with bfloat16 for Intel AMX

 

AI Tools

Accelerate data science and AI pipelines-from preprocessing through machine learning-and provide interoperability for efficient model development.

 

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Intel® oneAPI Deep Neural Network Library (oneDNN)

Improve deep learning (DL) application and framework performance on CPUs and GPUs with highly optimized implementations of DL building blocks.

 

Get It Now

 

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