3.6 Hardware Aware Training Preview¶
Role at Stage 3: Deep Learning¶
Understand the first layer of GPU utilization, memory pressure, and data bottlenecks. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.
Explanation¶
This part has 3 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.
Part Diagram¶
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flowchart LR
P["<b>3.6</b><br/>Hardware Aware<br/>Training Preview"]
P --> S1["<b>3.6.1</b><br/>GPU Utilization Basics"]
P --> S2["<b>3.6.2</b><br/>Memory Footprint"]
P --> S3["<b>3.6.3</b><br/>Profiling Training<br/>Runs"]
P --> E["<b>Exam</b><br/>Part practice"]Sub-Parts¶
| Sub-part folder | What it explains |
|---|---|
| 3.6.1 GPU Utilization Basics | GPU Utilization Basics 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. |
| 3.6.2 Memory Footprint | Memory Footprint 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. |
| 3.6.3 Profiling Training Runs | 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. |
What a Person Who Masters This Part Can Do¶
- Explain how Hardware Aware Training Preview supports a pytorch training project with loops, validation curves, checkpoints, ablations, and debugging notes..
- Build and inspect this artifact: Profile a small training run.
- Measure progress with: Track CPU/GPU utilization, dataloader time, batch size, and memory.
- Debug at least one failure mode before moving to the next part.
Build and Measure¶
Build: Profile a small training run.
Measure: Track CPU/GPU utilization, dataloader time, batch size, and memory.
Tests¶
Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.
Back to Stage¶
Return to Stage 3: Deep Learning.