Optimize the Latest Deep Learning Workloads using Intel-optimized PyTorch
 

Deep learning is massively popular in scientific computing, with DL algorithms used by industries to solve complex, computationally sophisticated problems in real time. Find out how two Intel oneAPI-optimized tools can boost training and inference performance of big models.

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.