Training-Run Automation
An agent chooses hyperparameters, then the server trains a real image classifier (circles vs squares) end-to-end with a live loss + accuracy curve, evaluates on held-out data, and the agent summarizes. Small but real — it demonstrates the orchestration, not large-scale pretraining.
How it works
- Plan: the agent picks learning rate + epochs for the task.
- Data: synthetic 12×12 grayscale images (circles vs squares) are generated server-side.
- Train: a logistic-regression classifier trains via gradient descent (NumPy) — loss + accuracy stream per epoch.
- Evaluate + summarize: held-out accuracy is computed; the agent explains the run.
Agent planshyperparams
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Trainlive metrics
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Evaluate
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Summarize