Train RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
RuView trains several kinds of model. Pick the track that matches the goal; all of them run on a laptop, with an optional GPU path.
Trains 17-keypoint pose from 10 sensor signals. Fast, fully unsupervised, modest accuracy.
cd v2
# Pretrain on raw CSI (contrastive)
cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50
# Train pose head, save an RVF artifact
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf
~84 s on an M4 Pro. Benchmarks: node scripts/benchmark-wiflow.js, eval: node scripts/eval-wiflow.js.
Uses a webcam + MediaPipe as ground truth, paired with ESP32 CSI. ~19 min on a laptop.
# 1. Collect paired data (camera + CSI)
python scripts/collect-ground-truth.py # MediaPipe pose landmarks
python scripts/collect-training-data.py # CSI capture, time-synced
node scripts/align-ground-truth.js # align camera ↔ CSI timestamps
# 2. Train (the camera-supervised path through the sensing-server / train crate)
cd v2
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/paired/ --epochs <N> --save-rvf model.rvf
# 3. Evaluate
cd .. && node scripts/eval-wiflow.js # reports PCK@20
Requires data/pose_landmarker_lite.task (MediaPipe model). See docs/adr/ADR-079-camera-ground-truth-training.md.
CSI subcarrier amplitude/phase → embeddings for re-ID and retrieval (171K emb/s on M4 Pro). Driven by wifi-densepose-train + wifi-densepose-ruvector (RuVector v2.0.4). Spectrogram embeddings: ADR-076.
cd v2
cargo check -p wifi-densepose-train --no-default-features # sanity
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env
Make a model transfer across environments without retraining. Configured through the training pipeline's domain-generalization options; see ADR-027 and wifi-densepose-train + ruview_metrics.
Spiking neural network that adapts to a new room in <30 s, on-device or on a Cognitum Seed:
node scripts/snn-csi-processor.js --port 5006
See docs/tutorials/cognitum-seed-pretraining.md, ADR-084/085 (RaBitQ similarity sensor), ADR-086 (edge novelty gate).
Project cognitum-20260110 has L4 / A100 / H100 quota.
gcloud auth login
gcloud config set project cognitum-20260110
bash scripts/gcloud-train.sh --dry-run # smoke test, synthetic data
bash scripts/gcloud-train.sh --gpu l4 --hours 2 # prototyping
bash scripts/gcloud-train.sh --gpu a100 --config scripts/training-config-sweep.json
bash scripts/gcloud-train.sh --sweep # full hyperparameter sweep
# VM is auto-deleted after training unless --keep-vm. Cost: L4 ~$0.80/hr, A100 40GB ~$3.60/hr.
Local Mac training: bash scripts/mac-mini-train.sh. Model benchmark: python scripts/benchmark-model.py.
python scripts/publish-huggingface.py # or: bash scripts/publish-huggingface.sh
Pushes the RVF artifact + card to Hugging Face. See docs/huggingface/.
| Path | Contents |
|---|---|
data/recordings/ | Raw CSI captures (*.csi.jsonl), overnight runs |
data/csi/ | CSI datasets for pretraining |
data/mmfi/ | MM-Fi dataset (ADR-015) |
data/paired/ | Camera ↔ CSI paired samples (ADR-079) |
data/ground-truth/ | MediaPipe pose landmarks |
data/pose_landmarker_lite.task | MediaPipe model file |
models/ | Trained artifacts |
Record more data: python scripts/record-csi-udp.py (UDP CSI capture from a live node).
cd v2 && cargo test --workspace --no-default-features # 1,400+ pass, 0 fail
cd .. && python archive/v1/data/proof/verify.py # VERDICT: PASS
Then hand off to ruview-verify for the witness bundle.
wifi-densepose-train, wifi-densepose-nn, wifi-densepose-ruvector, wifi-densepose-sensing-serverscripts/gcloud-train.sh, mac-mini-train.sh, benchmark-wiflow.js, eval-wiflow.js, benchmark-model.pyまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Web browser automation with AI-optimized snapshots for claude-flow agents
日本語の概要は準備中です。原文の説明を表示しています。