wifi-densepose/plugins/ruview/commands/ruview-train.md

description	argument-hint
Train a RuView model — camera-free WiFlow pose, camera-supervised pose (92.9% PCK@20), RuVector embeddings, domain generalization, local SNN, with optional GPU on GCloud.	[camera-free|camera-supervised|embeddings|domain-gen|snn|gpu] [--epochs N]

/ruview-train

Train, fine-tune, evaluate, or publish a RuView model.

1. Invoke the ruview-model-training skill.
2. Pick the track from $ARGUMENTS; if empty, ask which:
   • camera-free (Track A) — cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50 then -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf. ~84 s on M4 Pro, modest accuracy.
   • camera-supervised (Track B, ADR-079) — python scripts/collect-ground-truth.py, python scripts/collect-training-data.py, node scripts/align-ground-truth.js, then train on data/paired/, eval with node scripts/eval-wiflow.js. ~19 min, 92.9% PCK@20. Needs data/pose_landmarker_lite.task.
   • embeddings (Track C, AETHER ADR-024) — wifi-densepose-train + wifi-densepose-ruvector; -- --model model.rvf --embed, -- --build-index env.
   • domain-gen (Track D, MERIDIAN ADR-027) / snn (Track E) — node scripts/snn-csi-processor.js --port 5006; cognitum-seed-pretraining tutorial.
   • gpu — gcloud config set project cognitum-20260110; bash scripts/gcloud-train.sh --gpu l4 --hours 2 (or --gpu a100 --sweep, --dry-run to smoke-test). VM auto-deletes unless --keep-vm.
3. After training: cd v2 && cargo test --workspace --no-default-features, python archive/v1/data/proof/verify.py. To publish: python scripts/publish-huggingface.py.
4. Hand off to /ruview-verify for the witness bundle.