3.6.3 Profiling Training Runs¶
Why This Sub-Part Matters¶
Profiling Training Runs is the working skill inside Hardware Aware Training Preview that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result. A sub-part is now a folder so longer topics can grow without forcing everything into one huge page.
Study Pages¶
| Page | Purpose |
|---|---|
| Deep Dive | Full explanation, mechanisms, examples, and failure modes. |
| Examples and Practice | Worked exercises, project drills, and self-check prompts. |
Core Ideas¶
- Define Profiling Training Runs in plain language before naming tools or frameworks.
- Connect it to the stage artifact: A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes.
- Measure it with: CPU/GPU utilization, dataloader time, batch size, and memory
- Name at least one failure mode, because real AI engineering is mostly controlled failure reduction.
- Keep the first implementation small enough to inspect by hand before scaling it.
How to Study It¶
- Read this overview and write the concept in your own words.
- Read the deep dive and identify the input, transformation, output, and failure mode.
- Complete the examples and practice page.
- Add one measurement using: Track CPU/GPU utilization, dataloader time, batch size, and memory.
Completion Standard¶
- I can explain Profiling Training Runs without naming a tool first.
- I can connect it to the stage artifact.
- I can show a small artifact, measurement, or test.
- I know how it fails and what I would inspect first.
Return to 3.6 Hardware Aware Training Preview.