Module 2 – AI Runtime Engineering · 2
An explainable runtime capstone
15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.
Build a tiny graph executor with input, matrix multiplication, bias, and activation nodes. Validate shapes, keep a CPU reference, plan buffer lifetimes, and benchmark warm and cold runs separately. AGX Open currently provides a CPU reference backend; a requested GPU backend is not evidence of GPU execution. Add a backend only when detection and correctness checks prove it is used.
Practice and evidence: Predict the example or draw its state transitions. Change one assumption, test the boundary, and record the actual result. Explain what this small model does not establish about a production system.
Practice and evidence
Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.
Language reference: git.agilaos.com
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