fix(backup): corregir descarga y añadir restauración de BD
- Backend usa DATABASE_URL (resolvePgConnInfo) en vez de DB_* que no existían en el contenedor API, arreglando el fallo de pg_dump - Dockerfile backend: alpine 3.23 para pg_dump 18.4 (compatible con PostgreSQL 18 del servidor) - BackupDatabase ya no usa docker exec (no disponible en la imagen) - Nuevo endpoint POST /api/admin/restore para restaurar .sql o .zip vía psql - UI de Configuración: sección Restaurar Base de Datos - Regenerado go.sum para el build
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22 changed files with 7624 additions and 450 deletions
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@ -308,6 +308,7 @@ services:
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CT2_MODEL_PATH: /app/models/nllb-ct2
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CT2_DEVICE: cpu
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CT2_COMPUTE_TYPE: int8
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CT2_INTRA_THREADS: 4
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UNIVERSAL_MODEL: facebook/nllb-200-distilled-600M
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HF_HOME: /app/hf_cache
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TZ: Europe/Madrid
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@ -325,14 +326,13 @@ services:
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restart: unless-stopped
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# ==================================================================================
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# TRANSLATOR GPU - Multiple instances with --scale for parallel GPU processing
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# Configure multiple GPU workers: docker compose up -d --scale translator-gpu=2
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# TRANSLATOR CPU Nº2 - Segundo worker CPU (paralelo al translator)
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# ==================================================================================
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translator-gpu:
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build:
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context: .
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dockerfile: Dockerfile.translator-gpu
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image: rss2-translator-gpu:latest
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dockerfile: Dockerfile.translator
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image: rss2-translator:latest
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command: bash -lc "python -m workers.ctranslator_worker"
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environment:
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DB_HOST: db
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@ -341,70 +341,27 @@ services:
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DB_USER: ${DB_USER:-rss}
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DB_PASS: ${DB_PASS}
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TARGET_LANGS: es
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TRANSLATOR_BATCH: 256
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CT2_MODEL_PATH: /app/models/nllb-ct2-1.3b
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CT2_DEVICE: cuda
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CT2_COMPUTE_TYPE: float16
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UNIVERSAL_MODEL: facebook/nllb-200-1.3B
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TRANSLATOR_BATCH: 32
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CT2_MODEL_PATH: /app/models/nllb-ct2
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CT2_DEVICE: cpu
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CT2_COMPUTE_TYPE: int8
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CT2_INTRA_THREADS: 4
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UNIVERSAL_MODEL: facebook/nllb-200-distilled-600M
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HF_HOME: /app/hf_cache
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TZ: Europe/Madrid
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TRANSLATOR_ID: ${TRANSLATOR_ID:-}
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PYTORCH_CUDA_ALLOC_CONF: max_split_size_mb:512
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NCCL_DEBUG: INFO
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PYTORCH_ENABLE_MPS_FALLBACK: 1
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volumes:
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- ./workers:/app/workers
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- ./hf_cache:/app/hf_cache
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- ./models:/app/models
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- /dev/nvidia:/dev/nvidia
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- /usr/local/nvidia:/usr/local/nvidia:ro
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networks:
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- backend
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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: 1
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capabilities: [gpu]
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depends_on:
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db:
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condition: service_healthy
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restart: unless-stopped
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# ==================================================================================
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# REMOTE TRANSLATOR WORKER - Worker remoto que se conecta por WebSocket
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# ==================================================================================
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remote-translator:
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build:
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context: .
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dockerfile: Dockerfile.remote-worker
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container_name: rss2_remote_translator
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environment:
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WORKER_NAME: ${WORKER_NAME:-remote-worker-1}
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WORKER_API_KEY: ${WORKER_API_KEY:-}
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WORKER_SERVER: ${WORKER_SERVER:-ws://backend-go:8080/ws/worker}
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CT2_DEVICE: ${CT2_DEVICE:-cuda}
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CT2_MODEL_PATH: /app/models/nllb-ct2
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CT2_COMPUTE_TYPE: ${CT2_COMPUTE_TYPE:-float16}
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UNIVERSAL_MODEL: facebook/nllb-200-1.3B
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HF_HOME: /app/hf_cache
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TZ: Europe/Madrid
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volumes:
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- ./hf_cache:/app/hf_cache
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- ./models:/app/models
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networks:
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- backend
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deploy:
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resources:
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limits:
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memory: 6G
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [ gpu ]
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restart: unless-stopped
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# ==================================================================================
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# TRANSLATION SCHEDULER - Creates translation jobs
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# ==================================================================================
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@ -455,7 +412,7 @@ services:
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EMB_SLEEP_IDLE: 5
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EMB_LANGS: es
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EMB_LIMIT: 1000
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DEVICE: cuda
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DEVICE: cpu
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HF_HOME: /app/hf_cache
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TZ: Europe/Madrid
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volumes:
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@ -467,11 +424,6 @@ services:
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resources:
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limits:
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memory: 6G
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [ gpu ]
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depends_on:
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db:
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condition: service_healthy
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@ -523,7 +475,7 @@ services:
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RELATED_SLEEP: 10
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RELATED_BATCH: 200
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RELATED_TOPK: 10
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EMB_MODEL: mxbai-embed-large
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EMB_MODEL: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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TZ: Europe/Madrid
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networks:
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- backend
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@ -660,117 +612,8 @@ services:
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memory: 2G
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# ==================================================================================
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# LLM CATEGORIZER (Python) - Categorización con Ollama
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# REDES SEGMENTADAS
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# ==================================================================================
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llm-categorizer:
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build:
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context: .
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dockerfile: Dockerfile
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container_name: rss2_llm_categorizer
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command: bash -lc "python -m workers.simple_categorizer_worker"
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environment:
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DB_HOST: db
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DB_PORT: 5432
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DB_NAME: ${DB_NAME:-rss}
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DB_USER: ${DB_USER:-rss}
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DB_PASS: ${DB_PASS}
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CATEGORIZER_BATCH_SIZE: 10
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CATEGORIZER_SLEEP_IDLE: 5
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TZ: Europe/Madrid
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volumes:
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- ./workers:/app/workers
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networks:
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- backend
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depends_on:
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db:
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condition: service_healthy
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restart: unless-stopped
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deploy:
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resources:
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limits:
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cpus: '2'
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memory: 1G
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# ==================================================================================
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# MONITORING STACK - SECURED
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# ==================================================================================
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prometheus:
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image: prom/prometheus:latest
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container_name: rss2_prometheus
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volumes:
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- ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml:ro
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- prometheus_data:/prometheus
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command:
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- '--config.file=/etc/prometheus/prometheus.yml'
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- '--storage.tsdb.path=/prometheus'
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- '--web.console.libraries=/usr/share/prometheus/console_libraries'
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- '--web.console.templates=/usr/share/prometheus/consoles'
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# SEGURIDAD: Sin exposición de puertos - acceso solo vía Grafana o túnel SSH
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# ports:
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# - "9090:9090"
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networks:
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- monitoring
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restart: unless-stopped
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deploy:
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resources:
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limits:
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cpus: '1'
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memory: 2G
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grafana:
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image: grafana/grafana:latest
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container_name: rss2_grafana
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# SEGURIDAD: Acceso solo en localhost o vía túnel SSH
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# Para acceso remoto, usar túnel SSH: ssh -L 3001:localhost:3001 user@server
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ports:
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- "127.0.0.1:3001:3000"
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environment:
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# SEGURIDAD: Cambiar este password en producción
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- GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD:-change_this_password}
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- GF_USERS_ALLOW_SIGN_UP=false
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- GF_SERVER_ROOT_URL=http://localhost:3001
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- GF_SECURITY_COOKIE_SECURE=false
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- GF_SECURITY_COOKIE_SAMESITE=lax
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volumes:
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- grafana_data:/var/lib/grafana
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networks:
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- monitoring
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depends_on:
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- prometheus
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restart: unless-stopped
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deploy:
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resources:
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limits:
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cpus: '1'
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memory: 1G
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cadvisor:
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image: gcr.io/cadvisor/cadvisor:latest
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container_name: rss2_cadvisor
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# SEGURIDAD: Sin exposición de puertos - solo acceso interno
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# ports:
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# - "8081:8080"
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volumes:
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- /:/rootfs:ro
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- /var/run:/var/run:ro
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- /sys:/sys:ro
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- /var/lib/docker/:/var/lib/docker:ro
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- /dev/disk/:/dev/disk:ro
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devices:
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- /dev/kmsg
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networks:
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- monitoring
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restart: unless-stopped
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deploy:
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resources:
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limits:
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cpus: '0.5'
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memory: 512M
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# ==================================================================================
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# REDES SEGMENTADAS
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# ==================================================================================
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networks:
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# Red frontal - Solo nginx y web app
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frontend:
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@ -784,13 +627,5 @@ networks:
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driver: bridge
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internal: false # Acceso externo permitido (necesario para ingestor)
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# Red de monitoreo - Prometheus, Grafana, cAdvisor
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monitoring:
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name: rss2_monitoring
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driver: bridge
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internal: true
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volumes:
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prometheus_data:
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grafana_data:
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torch_extensions:
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