AI Analytics PART 2: Enhance Deep Learning Workloads on 3rd Gen Intel® Xeon® Scalable Processors

This webinar looks at the Intel® oneAPI AI Analytics Toolkit from the perspective of deep learning (DL) workloads, including the performance benefits and features that can enhance DL training, inference, and workflows.

Join software engineer Louis Tsai for this PART 2 session that delivers insights into the latest optimizations for Intel® Optimization for TensorFlow* and PyTorch which leverage the new acceleration instructions including Intel® DL Boost and BF16 support from 3rd Gen Intel® Xeon® Scalable processors.

Topics covered:

  • How to quantize a model from fp32/bf16 to int8 and analyze the performance speedup among different data types (fp32, bf16, and int8) in depth
  • Model Zoo for Intel® Architecture and low-precision tools included in the AI Kit
  • Efficiencies when building ML pipelines

Get the software
Download the Intel® oneAPI AI Analytics Toolkit for Linux. Find out more. Download now.

Other resources

  • Get the Jupyter notebooks in the first demo—These Jupyter notebooks help users analyze the performance benefit from using Intel Optimizations for Tensorflow with the oneDNN library.
  • Read the latest Intel AI Analytics blogs on Medium.
  • Develop in the Cloud—Sign up for an Intel® DevCloud account, a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Subscribe to the POD—Code Together is an interview series that explores the challenges at the forefront of cross-architecture development. Each bi-weekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Listen and subscribe today.
Louie Tsai, Software Engineer, Intel Corporation

Louie is a Senior Software Engineer in Intel’s Technical Computing, Analyzers and Runtimes group. He is responsible for driving customer engagements with and adoption for Intel® Performance Libraries, leveraging the synergies between Python* and the Intel® Math Kernel Library (Intel® MKL). In addition, Louie focuses on embedded applications, with particular focus on autonomous driving and helping customers optimize their Deep Learning-related workloads. Louie has a Master’s degree in Computer Science and Information Engineering from National Chiao Tung University.

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