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<!doctype html><html lang="en"><meta charset="utf-8"><title>An explainable runtime capstone</title><h1>An explainable runtime capstone</h1><p>15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.</p><p>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.</p><p><strong>Practice and evidence:</strong> 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.</p><h3>Practice and evidence</h3><p>Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.</p><p><a href="https://git.agilaos.com/matt/open_agx">Language reference: git.agilaos.com</a></p><p><a href="https://onnx.ai/onnx/intro/">Language reference: onnx.ai</a></p></html> |