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Optimize and Debug Your Model

Profiling and optimizing are important tasks that work together to transform your model from simply functional to highly efficient and performant. ​To get started, follow the Performance Optimization Guide Checklist.

​Optimizing a model falls into three main categories​:

  • Initial model porting ensures the model is functional on Intel® Gaudi® processors by running GPU migration. Follow the Get Started instructions for training or inference.​
    ​
  • Model optimizations include general enhancements for performance and apply to most models. This includes managing dynamic shapes and using HPU_graphs for training or inference​.
    ​
  • Profiling allows you to identify bottlenecks on the host CPU or on the Intel Gaudi processor. Follow the steps outlined in Table 1 by first using the TensorBoard* toolkit with the Intel Gaudi platform to identify specific items.

Table 1. Tasks, activities, and results to optimize your model

1. Perform PyTorch* profiling using TensorBoard

Obtains recommendations for performance using TensorBoard specific to Intel Gaudi accelerators

Profile with PyTorch

2. Review the PT_HPU_METRICS_FILE

Looks for excessive recompilations during runtime

Set HPU Metrics Review

3. Profile using a trace viewer for Intel Gaudi accelerators

Uses the accelerator-specific Perfetto trace viewer for in-depth analysis of CPU and accelerator activity

Get Started Profiling with Intel Gaudi Software

4. Perform model logging

Sets ENABLE_CONSOLE to set logging for debug and analysis

Runtime Environment Variables

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Additional Resources

Intel Gaudi Accelerator Performance Optimization Guide

Model Optimization Checklist

Manage Dynamic Shapes

Profile and Optimize


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