745a64b342
- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89% - Generation 50k spectres synthetiques 1D (12-24h durees) - Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell) - Detection continue avec soustraction du background - Capture background 24h avec gestion deconnexion - Docker Compose : conteneur train (GPU) + detect (CPU/USB) - Modele entraite inclus (vega_best.pt, 395 Mo) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
55 lines
1.3 KiB
YAML
55 lines
1.3 KiB
YAML
services:
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train:
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build:
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context: ./train
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dockerfile: Dockerfile
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volumes:
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- ./data:/data
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- ./models:/models
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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environment:
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- NVIDIA_VISIBLE_DEVICES=1
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- CUDA_VISIBLE_DEVICES=1
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- DATA_DIR=/data/synthetic
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- MODEL_DIR=/models
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- NUM_SAMPLES=50000
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- EPOCHS=100
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- BATCH_SIZE=64
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- LEARNING_RATE=0.001
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- DETECTOR=radiacode_103
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- MIN_DURATION=43200
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- MAX_DURATION=86400
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- SEED=42
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detect:
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build:
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context: ./detect
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dockerfile: Dockerfile
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volumes:
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- ./models:/models:ro
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- ./logs:/logs
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- ./data:/data
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devices:
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- /dev/bus/usb:/dev/bus/usb
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privileged: true
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environment:
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- MODEL_PATH=/models/vega_best.pt
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- ISOTOPE_INDEX_PATH=/models/vega_isotope_index.txt
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- BACKGROUND_PATH=/data/background_24h.npy
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- VEGA_ML_PATH=/models/vega_ml
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- VEGA_DEVICE=cpu
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- LOG_DIR=/logs
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- SAMPLE_INTERVAL=60
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- REPORT_HOUR=0
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- MIN_LIVE_TIME=3600
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- THRESHOLD=0.5
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depends_on:
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train:
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condition: service_completed_successfully
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restart: unless-stopped |