Examples and Practice: GPU Utilization Basics¶
Worked Practice¶
- Write one paragraph explaining GPU Utilization Basics to a beginner.
- Draw the smallest diagram that shows input, transformation, output, and failure mode.
- Build or outline a tiny artifact connected to: Profile a small training run.
- Measure it with: Track CPU/GPU utilization, dataloader time, batch size, and memory.
- Add one failure case to your learning log.
Mini Project Drill¶
Create a file named notes/gpu-utilization-basics.md in your project workspace. Include:
- the problem GPU Utilization Basics solves
- the simplest implementation or design
- the measurement you used
- one example input
- one expected output
- one failure case
- one decision you would make from the result
Check Your Understanding¶
| Question | What a strong answer includes |
|---|---|
| Why does GPU Utilization Basics matter? | It connects to a pytorch training project with loops, validation curves, checkpoints, ablations, and debugging notes. and names a practical risk. |
| How would you test it? | It uses a small repeatable case and a measurable expected result. |
| What breaks first? | It names a specific failure mode, not only "the model is bad". |
| When should you move on? | When the artifact works on a realistic case and one edge case. |
Stretch Exercise¶
Revisit the same drill after finishing the next part. Update the note with what changed. This is how isolated concepts become connected system judgment.
Return to 3.6.1 GPU Utilization Basics.