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wifi-densepose/plugins/ruview/codex/prompts/ruview-train.md

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/ruview-train — train a RuView model

Train / evaluate / publish a RuView model. Track: $ARGUMENTS (one of camera-free, camera-supervised, embeddings, domain-gen, snn, gpu; if empty, ask).

  • camera-free (WiFlow pose, no labels): cd v2 && 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. Bench node scripts/benchmark-wiflow.js, eval node scripts/eval-wiflow.js.
  • camera-supervised (ADR-079, 92.9% PCK@20, ~19 min): python scripts/collect-ground-truth.py (MediaPipe landmarks; needs data/pose_landmarker_lite.task), python scripts/collect-training-data.py (CSI capture), node scripts/align-ground-truth.js (timestamp align), then cd v2 && cargo run -p wifi-densepose-sensing-server -- --train --dataset data/paired/ --epochs <N> --save-rvf model.rvf, eval node scripts/eval-wiflow.js (reports PCK@20).
  • embeddings (AETHER ADR-024 / spectrogram ADR-076): wifi-densepose-train + wifi-densepose-ruvector; -- --model model.rvf --embed, -- --model model.rvf --build-index env. 171K emb/s on M4 Pro.
  • domain-gen (MERIDIAN ADR-027): domain-generalization options in the training pipeline + ruview_metrics.
  • snn (local env adaptation, <30 s): node scripts/snn-csi-processor.js --port 5006; docs/tutorials/cognitum-seed-pretraining.md; ADR-084/085 (RaBitQ), ADR-086 (novelty gate).
  • gpu: gcloud auth login && gcloud config set project cognitum-20260110, then bash scripts/gcloud-train.sh --dry-run (smoke), bash scripts/gcloud-train.sh --gpu l4 --hours 2 (proto, ~$0.80/hr), bash scripts/gcloud-train.sh --gpu a100 --config scripts/training-config-sweep.json (~$3.60/hr), bash scripts/gcloud-train.sh --sweep (full sweep). VM auto-deletes unless --keep-vm. Local Mac: bash scripts/mac-mini-train.sh. Bench: python scripts/benchmark-model.py.

Data: data/recordings/ raw CSI · data/csi/ pretrain · data/mmfi/ MM-Fi · data/paired/ camera↔CSI · data/ground-truth/ MediaPipe · models/ artifacts. Record more: python scripts/record-csi-udp.py.

After training: cd v2 && cargo test --workspace --no-default-features, cd .. && python archive/v1/data/proof/verify.py (VERDICT: PASS). Publish: python scripts/publish-huggingface.py (or .sh; docs/huggingface/). Then run /ruview-verify.