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13 KiB
13 KiB
Progetto Container Docker ble-ai-localizer
Ambiente: MN reslevis 192.168.1.3
m1.MajorNet-x64.6.6.0-60.bin:03 October 2025
server linux gentoo kernl: 6.6.74-gentoo-x86_64 Python default version 3.10.16
cd /data/service/
###########################################################################################
Passo 1 Creazione struttura progetto (sul server)
mkdir -p ble-ai-localizer/{app,config,data,models}
cd /data/service/ble-ai-localizer
touch Dockerfile requirements.txt entrypoint.sh docker-compose.yml
touch app/{__init__.py,main.py,settings.py,auth.py,mqtt_client.py,train.py,infer.py}
touch config/config.yaml config/secrets.yaml
Cosa hai ottenuto
app/ codice python (training + inference)
config/ configurazione (config.yaml versionabile + secrets.yaml non versionabile)
data/ dataset fingerprint (volume)
models/ modello salvato (volume)
###########################################################################################
Passo 2 .gitignore (consigliato subito)
Se usi git:
cat > .gitignore << 'EOF'
# secrets e artefatti
config/secrets.yaml
data/
models/
# python
__pycache__/
*.pyc
.venv/
# editor
.vscode/
.idea/
EOF
###########################################################################################
Passo 3 Verifica prerequisiti Docker (Gentoo)
Esegui:
docker --version
docker compose version
Se docker compose non esiste, di solito su Gentoo:
o hai il plugin docker-cli-compose
o usi docker-compose (legacy). In tal caso dimmelo e ti adatto i comandi.
Quando hai eseguito i comandi del Passo 1–3, nel prossimo step ti faccio creare:
requirements.txt minimale + pinned
Dockerfile compatibile (python:3.10-slim)
docker-compose.yml con volumi (config/, data/, models/) e variabile MODE=train|infer
Se vuoi andare avanti subito senza feedback, incolla qui l’output di:
docker --version
docker compose version
docker compose version
Docker version 27.4.1, build b9d17eaebb55b7652ce37ae5c7c52fcb34194956
Docker Compose version v2.28.1
Perfetto, sei a posto (Docker 27.4.1 + Compose v2.28.1).
###########################################################################################
Passo 4 — Popoliamo i file base (requirements, Dockerfile, entrypoint, compose)
4.1 requirements.txt
Metti dentro (pinned e minimale per il tuo caso):
cat > requirements.txt << 'EOF'
pandas==2.2.2
numpy==1.26.4
scikit-learn==1.5.1
joblib==1.4.2
paho-mqtt==2.1.0
requests==2.32.3
PyYAML==6.0.2
fastapi==0.115.0
uvicorn[standard]==0.30.6
matplotlib==3.9.2
seaborn==0.13.2
EOF
(Se poi decidiamo che plot non servono in produzione, li togliamo.)
4.2 Dockerfile (Python “fissato”, non-root, robusto)
cat > Dockerfile << 'EOF'
FROM python:3.10-slim
# dipendenze native utili a numpy/scikit (safe choice)
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential gcc g++ \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY requirements.txt /app/requirements.txt
RUN pip install --no-cache-dir -r /app/requirements.txt
COPY app/ /app/app/
COPY entrypoint.sh /app/entrypoint.sh
RUN chmod +x /app/entrypoint.sh
# utente non-root
RUN useradd -m appuser
USER appuser
# cartelle dati/modelli (volumi)
RUN mkdir -p /home/appuser/data /home/appuser/models
ENV DATA_DIR=/home/appuser/data
ENV MODELS_DIR=/home/appuser/models
ENTRYPOINT ["/app/entrypoint.sh"]
EOF
4.3 entrypoint.sh (MODE=train|infer|api)
cat > entrypoint.sh << 'EOF'
#!/usr/bin/env bash
set -euo pipefail
MODE="${MODE:-infer}"
case "$MODE" in
train)
exec python -m app.main train
;;
infer)
exec python -m app.main infer
;;
api)
exec uvicorn app.main:api --host 0.0.0.0 --port "${PORT:-8080}"
;;
*)
echo "MODE non valido: $MODE (usa train|infer|api)"
exit 1
;;
esac
EOF
Poi rendilo eseguibile:
chmod +x entrypoint.sh
4.4 docker-compose.yml (volumi + config esterna)
cat > docker-compose.yml << 'EOF'
services:
ble-ai-localizer:
build: .
image: ble-ai-localizer:0.1.0
environment:
MODE: "infer"
CONFIG_FILE: "/app/config/config.yaml"
SECRETS_FILE: "/app/config/secrets.yaml"
volumes:
- ./config:/app/config:ro
- ./data:/data
- ./models:/models
restart: unless-stopped
EOF
4.5 config/config.yaml (scheletro)
cat > config/config.yaml << 'EOF'
mqtt:
host: "mosquitto"
port: 1883
topic: "ble/raw"
api:
get_gateways_url: "https://APIHOST:5050/reslevis/getGateways"
verify_tls: false
refresh_seconds: 300
oidc:
token_url: "https://KEYCLOAK/realms/REALM/protocol/openid-connect/token"
client_id: "Fastapi"
audience: "Fastapi"
paths:
dataset: "/data/fingerprint.parquet"
model: "/models/model.joblib"
ml:
# parametri generali; l'algoritmo è un dettaglio interno
method: "knn"
k: 7
weights: "distance"
metric: "euclidean"
EOF
4.6 config/secrets.yaml (placeholder, non versionare)
cat > config/secrets.yaml << 'EOF'
oidc:
client_secret: "CHANGE_ME"
username: "CHANGE_ME"
password: "CHANGE_ME"
EOF
###########################################################################################
Passo 5 Metti un main minimale per verificare che il container parte
app/main.py
cat > app/main.py << 'EOF'
from fastapi import FastAPI
from .settings import load_settings
api = FastAPI()
@api.get("/health")
def health():
return {"status": "ok"}
def main():
import sys
settings = load_settings()
print("Settings loaded. Keys:", list(settings.keys()))
if len(sys.argv) < 2:
raise SystemExit("Usage: python -m app.main [train|infer]")
cmd = sys.argv[1].lower()
if cmd == "train":
print("TRAIN mode (placeholder)")
elif cmd == "infer":
print("INFER mode (placeholder)")
else:
raise SystemExit("Unknown command")
if __name__ == "__main__":
main()
EOF
app/settings.py
cat > app/settings.py << 'EOF'
import os
from pathlib import Path
import yaml
def _read_yaml(path: str) -> dict:
with open(path, "r", encoding="utf-8") as f:
return yaml.safe_load(f) or {}
def deep_merge(a: dict, b: dict) -> dict:
out = dict(a or {})
for k, v in (b or {}).items():
if isinstance(v, dict) and isinstance(out.get(k), dict):
out[k] = deep_merge(out[k], v)
else:
out[k] = v
return out
def load_settings() -> dict:
cfg_path = os.getenv("CONFIG_FILE", "/app/config/config.yaml")
settings = _read_yaml(cfg_path)
secrets_path = os.getenv("SECRETS_FILE", "")
if secrets_path and Path(secrets_path).exists():
secrets = _read_yaml(secrets_path)
settings = deep_merge(settings, secrets)
# fallback paths (coerenti con compose)
settings.setdefault("paths", {})
settings["paths"].setdefault("dataset", os.getenv("DATASET_PATH", "/data/fingerprint.parquet"))
settings["paths"].setdefault("model", os.getenv("MODEL_PATH", "/models/model.joblib"))
return settings
EOF
(Gli altri file per ora possono restare vuoti.)
###########################################################################################
Passo 6 Build & test smoke test
docker compose build
###########################################################################################
### END prima creazione container #################
###########################################################################################
###########################################################################################
1) Come avviare SOLO ble-ai-localizer (senza impattare gli altri)
Vai nella directory del progetto:
cd /data/service/ble-ai-localizer
Avvio in background:
docker compose -p ble-ai-localizer up -d
Perché -p ble-ai-localizer?
Imposta esplicitamente il project name, così sei sicuro al 100% di nonagganciare per errore un altro compose.
Verifica stato (solo di questo progetto):
docker compose -p ble-ai-localizer ps
Log (solo di questo progetto):
docker compose -p ble-ai-localizer logs -f
2) Come stoppare/riavviare SOLO ble-ai-localizer
Stop (non rimuove container):
docker compose -p ble-ai-localizer stop
Restart:
docker compose -p ble-ai-localizer restart
Stop + rimozione container/network del progetto (NON tocca volumi bind ./data, ./models):
docker compose -p ble-ai-localizer down
3) Aggiornare solo il tuo container (codice o Dockerfile cambiato)
Ricostruisci l'immagine:
docker compose -p ble-ai-localizer build
Riavvia applicando l'immagine nuova:
docker compose -p ble-ai-localizer up -d
Se vuoi forzare rebuild+restart in un colpo:
docker compose -p ble-ai-localizer up -d --build
4) Esportare l'immagine (backup o deploy su altro server)
Esempio export in tar (meglio gzippato):
docker save ble-ai-localizer:0.1.0 | gzip > ble-ai-localizer_0.1.0.tar.gz
Su altro server:
gzip -dc ble-ai-localizer_0.1.0.tar.gz | docker load
#Debug
mosquitto_sub -v -h 192.168.1.101 -p 1883 -t '#' -V mqttv311 | grep publish_out
docker compose -p ble-ai-localizer exec -T ble-ai-localizer ls -l /data/config/
#Caso di gateway che non rileva nessun beacon
publish_out/ac233fc1dcd3 [{"timestamp":"2026-01-30T13:40:08.885Z","type":"Gateway","mac":"AC233FC1DCD3","nums":0}]
#Verifica del modello in uso:
#Ver 1
docker compose -p ble-ai-localizer exec -T ble-ai-localizer python - <<'PY'
import hashlib, joblib, os
p="/data/model/model.joblib"
b=open(p,"rb").read()
print(f"FILE: {p}")
print(f"sha256={hashlib.sha256(b).hexdigest()[:12]} size={len(b)} bytes")
m=joblib.load(p)
print("TYPE:", type(m))
for k in ["version","nan_fill","k_floor","k_xy","weights","metric","floors"]:
print(f"{k}:", getattr(m,k,None))
gws=getattr(m,"feature_gateways",[])
print("gateways:", len(gws), "first:", gws[:5])
regs=getattr(m,"xy_regs",{})
print("xy_regs floors:", sorted(list(regs.keys())))
PY
service "ble-ai-localizer" is not running
#Ver 2
docker compose -p ble-ai-localizer exec -T ble-ai-localizer python - <<'PY'
import joblib, pprint
m = joblib.load("/data/model/model.joblib")
keys = [
"created_at_utc","sklearn_version","numpy_version",
"gateways_order","nan_fill","k_floor","k_xy","weights","metric","floors"
]
pprint.pprint({k: m.get(k) for k in keys})
PY
#Esempio utilizzo server API:
Ottenimento del token:
TOKEN=$(
curl -k -s -X POST "https://10.251.0.30:10002/realms/API.Server.local/protocol/openid-connect/token" \
-H "Content-Type: application/x-www-form-urlencoded" \
-d "grant_type=password" \
-d "client_id=Fastapi" \
-d "client_secret=wojuoB7Z5xhlPFrF2lIxJSSdVHCApEgC" \
-d "username=core" \
-d "password=C0r3_us3r_Cr3d3nt14ls" \
-d "audience=Fastapi" \
| jq -r '.access_token'
)
Utilizzare il token in un API:
curl -k -X 'GET' \
'https://10.251.0.30:5050/reslevis/getTrackers' \
-H 'accept: application/json' \
-H 'Authorization: Bearer $TOKEN'
#Aggiornamento del software
cd /data/service/ble-ai-localizer
docker compose up -d --build
docker compose -p ble-ai-localizer build
docker compose -p ble-ai-localizer up -d --build
docker system prune
docker rmi ble-ai-localizer:0.1.0
Gestione Sart/Stop Container
cd /data/service/ble-ai-localizer
docker compose -p ble-ai-localizer up -d
docker compose -p ble-ai-localizer logs -f --tail=200 --timestamps
docker compose -p ble-ai-localizer stop
docker compose -p ble-ai-localizer restart
docker compose -p ble-ai-localizer down
Accesso Web MajorNET ResLevis:
https://10.251.0.30/frontend/app_reslevis/app.html#home
Accesso Web a Container ble-ai-localizer
URL: http://0.0.0.0:8501
http://192.168.1.3:8501/
username e password da file composer: docker-compose.yml
UI_USER: "Admin"
UI_PASSWORD: "pwdadmin1" <-- facilitate per accesso
da mobile
NOTE:
- Il valore di k utilizzato rappresenta la retta (k=2) o il piano (k>2) tra i
beacon di trainig, in addestramento deve coincidere come minimo con il
numero di beacon per stanza, ma è un parametro globale per cui va fatto
rispettare per tutte le stanze, per cui ad esempio se si decide di catturare 5
misure per stanza k conviene metterlo a 3
- almeno a 1m dalle pareti della stanza (evitare attaccato al muro su pareti
adiacenti)
- per piani simemtrici le misure di ogni piano conviene farle conincidere
- aggingere timestamp in fingerprint e registarre traffico mqtt raw (a
parita' di finestra di registrazione potranno essere rivalutati i valori
letti)
- Tempo di tx nel beacon 200 ms
- potenze 0, -4 ,-8, -12
- slot di raccolta 30s
- time 1400
- Inferenza a 7 sec
- durante la raccolta occorre almeno un beacon di test per potenza che veiene escluso
nell'addestarmento ma usato per l'inferenza con il traffico registrato nella
fase di raccolta.
- testare prima gw e mgtt se regge 200 ms
ORGANIZZAZIONE CAMPAGNA:
N.4 piedistalli con a bordo n.4 becon configurati ognuno:
- Tempo di tx nel beacon 200 ms
- potenze 0, -4 ,-8, -12
Organizzazione dei piedistalli:
GBC-01: BC-00-21, BC-04-22, BC-08-23, BC-12-24
GBC-02: BC-00-25, BC-04-26, BC-08-27, BC-12-28
GBC-03: BC-00-36, BC-04-37, BC-08-38, BC-12-39
GBC-14: BC-00-29, BC-04-30, BC-08-41, BC-12-42