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

Continuing the momentum from August 5th, this webinar (which is Part 2 in a 3-part series) looks at the Intel® AI Analytics Toolkit from the perspective of deep learning (DL) workloads.

As in … 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® 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.

For more complete information about compiler optimizations, see our Optimization Notice.