AI Performance Tuning Guide: Maximize the Potential of Intel® Hardware
AI Performance Tuning Guide: Maximize the Potential of Intel® Hardware
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Overview
Common challenges for AI developers are:
- Speeding up big networks to achieve real-time performance.
- Efficiently using hardware resources to save time and compute power.
- 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
Featured Software
- Get the Intel Extension of PyTorch as a stand-alone component from GitHub* or as part of the AI Tools and Frameworks.
- Get Intel VTune Profiler as a stand-alone component or as part of the Intel® oneAPI Base Toolkit.