The complexity of deep learning models is surging, warranting enhanced training and inference in distributed compute environments. This session focuses on the essential techniques to use with Intel® Data Center GPUs and CPUs to balance distributed AI workloads and meet data center challenges to improve advances in efficiency and performance.

Within the session, explore the Intel® Extension for PyTorch*, which optimizes neural network operations on Intel hardware, and learn how Microsoft DeepSpeed* can be integrated to perform training operations at scale.

The topics covered include:

• Tackle model scalability in a distributed environment skillfully, handling workloads efficiently across Intel Data Center GPUs and CPUs.
• Gain familiarity with essential tools from Intel to simplify operations, including PyTorch Distributed Data Parallel (DPP), Intel® openAPI Collective Communications Library (oneCCL), and the DeepSpeed library that streamlines network training at scale.
• Deploy practical solutions that maximize hardware efficiency and perfect strategies that ensure top performance for AI development.
• Sample code and see benchmarking milestones, using tools such as IPEX-LLM, to illustrate performance achievements.

Skill level: All skill levels

Get the Software

• Intel oneAPI Collective Communications Library
• Intel Extension for PyTorch from GitHub or AI Frameworks and Tools
• Intel® Extension for DeepSpeed*

Download Code Samples

• Get Started with Intel Extension for PyTorch
• See All Code Samples