Common challenges for AI developers are:

1. Speeding up big networks to achieve real-time performance.
2. Efficiently using hardware resources to save time and compute power.
3. Debugging the AI “black box” to identify which API calls are consuming the highest amount of resources.

This webinar addresses all three issues using just two tools: Intel® Extension for PyTorch* and Intel® VTune™ Profiler.

Key topics covered:

• How to use the PyTorch extension to access the latest hardware optimizations and enhance an AI application with minimal code changes (applied to a real-world example)
• How to further tune an application using hardware and software configurations; the result is more economical use of hardware resources
• How to use TorchServe with Intel Extension for PyTorch for efficiently serving and scaling PyTorch models
• How Intel VTune Profiler can find hot spots in your code and recommend ways to fix them and further optimize the application

Includes a live demo.

Skill level: Intermediate