version: '3.8'

# Stable Audio Open - AI Audio Generation
# Phase: ML Infrastructure
# Ports: 8000 (Gradio Web UI)
# GPU: YES - Requires NVIDIA Container Toolkit (8GB+ VRAM)
# Storage: SSD recommended for model cache
# Image: Built locally from https://github.com/SaladTechnologies/stable-audio-open

services:
  stable-audio:
    image: stable-audio-open:local
    container_name: stable-audio
    restart: unless-stopped
    ports:
      - "11500:8000"  # Gradio Web UI (internal only)
    volumes:
      # HuggingFace cache for model weights (~6GB)
      - /home/jpmschweitzer/docker-data/stable-audio/hf-cache:/root/.cache/huggingface
    environment:
      - TZ=Europe/Amsterdam
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
      - HF_TOKEN=${HF_TOKEN}
    healthcheck:
      test: ["CMD-SHELL", "curl -fSs http://localhost:8000/ || exit 1"]
      interval: 60s
      timeout: 30s
      retries: 3
      start_period: 300s  # Model download + load takes time
    deploy:
      resources:
        limits:
          memory: 16G
        reservations:
          memory: 8G
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    networks:
      - docker-dataplane

networks:
  docker-dataplane:
    external: true
    name: docker-dataplane

# GPU Requirements:
# - RTX 2080 Ti (11GB VRAM) - minimum viable, may struggle with long generations
# - Recommended: 16GB+ VRAM for reliable 47s audio generation
# - NVIDIA Container Toolkit must be installed
#
# IMPORTANT: GPU Contention
# - This service shares GPU with Ollama and Jellyfin
# - Consider stopping Ollama when generating audio for better VRAM availability
# - Monitor with: watch -n 1 nvidia-smi
#
# Prerequisites:
# 1. Build image: cd /home/jpmschweitzer/docker-data/stable-audio-open && docker build -t stable-audio-open:local .
# 2. Create HuggingFace token: https://huggingface.co/settings/tokens (read access)
# 3. Accept model license: https://huggingface.co/stabilityai/stable-audio-open-1.0
# 4. Create data directories:
#    mkdir -p /home/jpmschweitzer/docker-data/stable-audio/{hf-cache,output}
#
# After Deployment:
# 1. Set HF_TOKEN in Portainer environment variables
# 2. Deploy stack via Portainer
# 3. First run downloads model weights (~6GB) - be patient
# 4. Verify GPU access: docker exec stable-audio nvidia-smi
# 5. Access Web UI: http://tower-of-joy:11500 or http://audio.schweitz.internal (via NPM)
#
# NPM Configuration (audio.schweitz.internal):
# - Domain: audio.schweitz.internal
# - Scheme: http
# - Forward Hostname: stable-audio (or tower-of-joy)
# - Forward Port: 11500
# - Block Common Exploits: Yes
# - Websockets Support: Yes (required for Gradio)
#
# API Usage (Gradio API):
# curl -X POST http://audio.schweitz.internal/api/predict \
#   -H "Content-Type: application/json" \
#   -d '{"data": ["epic orchestral music, cinematic", 30, 100, 7]}'
# Parameters: [prompt, duration_seconds, diffusion_steps, cfg_scale]
#
# Rebuilding Image:
# cd /home/jpmschweitzer/docker-data/stable-audio-open
# git pull
# docker build -t stable-audio-open:local .