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Context: the project archive (KXKM_Batterie_Parallelator-main) had no git history locally; a fresh repository is needed to host it on git.saillant.cc (electron/KXKM_Batterie_Parallelator). Approach: initialize a new repo on branch main, stage the archive content, and harden .gitignore before the first commit. Changes: - Import the full project tree: firmware/, firmware-idf/, firmware-rs/, iosApp/, kxkm-bmu-app/, kxkm-api/, hardware/, docs/, specs/, scripts/, models/, tests/ - Keep project dotfiles tracked despite the trailing '.*' ignore rule: .github/, .claude/, .superpowers/, .gitattributes, .markdownlint.json - Extend .gitignore: firmware/src/credentials.h (local secrets, template kept), kxkm-bmu-app/**/build/ (66 MB compiled iOS framework), .remember/ (session data) Impact: the project can now be maintained on the self-hosted Gitea forge with a clean, secret-free initial history.
86 lines
2.5 KiB
Markdown
86 lines
2.5 KiB
Markdown
# scripts/ml
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ML pipeline scripts for BMU advisory intelligence (SOH/RUL).
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Safety rule:
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- Outputs are advisory only.
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- Firmware protections remain authoritative.
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## Main workflow
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1. Feature extraction
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```bash
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python3 scripts/ml/extract_features.py --input models/consolidated.parquet --output models/features_v2.parquet --window 60
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```
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2. Feature adaptation
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```bash
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python3 scripts/ml/adapt_features.py --input models/features_v2.parquet --output models/features_adapted_v2.parquet
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```
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3. SOH model training
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```bash
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python3 scripts/ml/train_fpnn.py --input models/features_adapted_v2.parquet --output-dir models --epochs 50 --hidden 64 --degree 2 --soh-mode capacity --test-device k-led1 --val-ratio 0.1
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```
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4. Quantization
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```bash
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python3 scripts/ml/quantize_tflite.py --model models/fpnn_soh.pt --features models/features_adapted_v2.parquet --output models/fpnn_soh_v2_quantized.onnx --backend onnxrt
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```
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5. Final metrics
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```bash
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python3 scripts/ml/finalize_phase2_metrics.py --features models/features_adapted_v2.parquet --quantized models/fpnn_soh_v2_quantized.onnx --rul-model models/rul_sambamixer.pt --train-log phase2_fpnn_train_v2.log --output models/phase2_metrics.json
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```
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## Remote execution
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Use orchestrator:
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```bash
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scripts/ml/remote_kxkm_ai_pipeline.sh check
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scripts/ml/remote_kxkm_ai_pipeline.sh bootstrap-container
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scripts/ml/remote_kxkm_ai_pipeline.sh discover-dataset
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scripts/ml/remote_kxkm_ai_pipeline.sh run-container
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```
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## Artifacts
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- models/features_v2.parquet
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- models/features_adapted_v2.parquet
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- models/fpnn_soh_v2_quantized.onnx
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- models/phase2_metrics.json
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## Quantization iteration
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Advanced quantization iteration:
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```bash
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python3 scripts/ml/quantize_tflite.py \
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--model models/fpnn_soh.pt \
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--features models/features_adapted_v2.parquet \
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--output models/fpnn_soh_v2_quantized.onnx \
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--backend onnxrt \
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--quant-format qdq \
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--calib-strategy stratified \
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--calib-samples 500 \
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--percentile-clip 1 99
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```
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Bootstrap verification now checks `pandas`, `numpy`, `torch`, `onnxruntime`, `pyarrow`, and `onnx`.
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Latest verified remote status on `kxkm-ai`:
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- Technical unblock: done.
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- Remaining blocker: quantized quality gate on the latest remote dataset split.
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## Promoted baseline on kxkm-ai
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Winning configuration promoted on `2026-03-30 16:17 UTC`:
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- `--quant-format qdq`
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- `--calib-strategy stratified`
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- `--calib-samples 500`
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- `--percentile-clip 1 99`
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Promoted remote metrics:
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- float32 MAPE: `7.7289%`
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- quantized MAPE: `10.7734%`
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- degradation: `+3.0446 pp`
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- quantized size: `15.99 KB`
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- `overall_gate_pass=true` |