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1.4 KiB
HTML
6 lines
1.4 KiB
HTML
<!doctype html><html lang="en"><meta charset="utf-8"><title>What an AI runtime does</title><h1>What an AI runtime does</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>A model describes parameters and operations. A runtime loads that representation, validates shapes and types, allocates memory, schedules operations, and executes kernels. Training and inference are different workflows. Begin with a tiny deterministic computation so correctness can be checked by hand before introducing acceleration.</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><pre><code class="language-python">x=[2.0,3.0]
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w=[0.5,2.0]
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bias=1.0
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y=sum(a*b for a,b in zip(x,w))+bias
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print(y)
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</code></pre><p><strong>Expected result:</strong> <code>8.0</code></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> |