Optimize the Latest Deep Learning Workloads using Intel-optimized PyTorch

For developers focused on deep learning use cases—predictive modeling, recommendation systems, natural language processing, object detection, and tons more—it’s paramount to extract the most workload performance using newer technologies like BF16, graph-level optimizations, and custom kernels.

This session focuses on the performance and ease-of-use benefits for DL training and inference of big models like DLRM (deep learning recommendation model) using Intel® Extension for PyTorch* and Intel® oneAPI Deep Neural Network Library (oneDNN).

Register to hear Senior Deep Learning Engineer Eikan Wang cover:

  • Using oneDNN to deliver optimal training and inference workload performance for the PyTorch framework on Intel hardware
  • oneDNN-based graph optimizations and custom kernel implementations to boost performance of DLRM modules in PyTorch
  • How Intel’s optimized PyTorch extension library can be dynamically loaded as a Python module to offer a more modular design for custom compound operations that are critical to accelerating key DL modules, e.g., the interaction module from DLRM.

Download the software

Resources

  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Explore oneAPI, including developer opportunities and benefits
  • 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. Available wherever you get your podcasts.

 

 

 

Eikan Wang, Sr. Deep Learning Engineer, Intel Corporation

Eikan is a senior software engineer at Intel® architecture, Graphics and Software group where he is the tech lead on PyTorch framework optimization for Intel Architecture and one of the major contributors to low-precision inference solutions on IA. He has 4 years of full-stack experience in artificial intelligence from various AI applications to framework, library, and compiler optimizations. Eikan received his bachelor’s degree in mathematics from Huaiyin Institute of Technology.

Performance varies by use, configuration, and other factors. Learn more at www.Intel.com/PerformanceIndex.