Pi has 4 cores; only KNN and tree-based models are realistic to train
here without GPU. While Lambda runs the full 16-job manifest in
parallel (~1.7h), this chain trains the CPU-friendly subset on the
Pi (~30 min) so scenes 8 & 12 populate with multi-model numbers
within minutes instead of waiting on Lambda's full cycle.
Order: gbt-realistic, knn-realistic, knn-oracle, knn_semi-realistic,
knn_semi-oracle. Skips models whose .ckpt.json already exists
(idempotent restart). Each is a subprocess of training/trainer/run.py
so XGBoost/numpy/sklearn don't fight each other for cores.
Caller is expected to start gbt-oracle separately (it's the longest
single training and we kicked it off before invoking this script).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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