feat(stack): add Stable Audio and TRELLIS GPU service stacks
- Stable Audio Open: AI audio generation on port 11500 (~6GB VRAM) - TRELLIS: 3D model generation on port 11510 (~6-8GB VRAM, low-VRAM fork) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.6
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version: '3.8'
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# Stable Audio Open - AI Audio Generation
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# Phase: ML Infrastructure
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# Ports: 8000 (Gradio Web UI)
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# GPU: YES - Requires NVIDIA Container Toolkit (8GB+ VRAM)
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# Storage: SSD recommended for model cache
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# Image: Built locally from https://github.com/SaladTechnologies/stable-audio-open
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services:
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stable-audio:
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image: stable-audio-open:local
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container_name: stable-audio
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restart: unless-stopped
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ports:
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- "11500:8000" # Gradio Web UI (internal only)
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volumes:
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# HuggingFace cache for model weights (~6GB)
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- /home/jpmschweitzer/docker-data/stable-audio/hf-cache:/root/.cache/huggingface
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environment:
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- TZ=Europe/Amsterdam
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility
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- HF_TOKEN=${HF_TOKEN}
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healthcheck:
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test: ["CMD-SHELL", "curl -fSs http://localhost:8000/ || exit 1"]
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interval: 60s
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timeout: 30s
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retries: 3
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start_period: 300s # Model download + load takes time
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deploy:
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resources:
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limits:
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memory: 16G
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reservations:
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memory: 8G
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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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networks:
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- docker-dataplane
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networks:
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docker-dataplane:
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external: true
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name: docker-dataplane
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# GPU Requirements:
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# - RTX 2080 Ti (11GB VRAM) - minimum viable, may struggle with long generations
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# - Recommended: 16GB+ VRAM for reliable 47s audio generation
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# - NVIDIA Container Toolkit must be installed
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#
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# IMPORTANT: GPU Contention
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# - This service shares GPU with Ollama and Jellyfin
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# - Consider stopping Ollama when generating audio for better VRAM availability
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# - Monitor with: watch -n 1 nvidia-smi
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#
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# Prerequisites:
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# 1. Build image: cd /home/jpmschweitzer/docker-data/stable-audio-open && docker build -t stable-audio-open:local .
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# 2. Create HuggingFace token: https://huggingface.co/settings/tokens (read access)
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# 3. Accept model license: https://huggingface.co/stabilityai/stable-audio-open-1.0
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# 4. Create data directories:
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# mkdir -p /home/jpmschweitzer/docker-data/stable-audio/{hf-cache,output}
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#
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# After Deployment:
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# 1. Set HF_TOKEN in Portainer environment variables
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# 2. Deploy stack via Portainer
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# 3. First run downloads model weights (~6GB) - be patient
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# 4. Verify GPU access: docker exec stable-audio nvidia-smi
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# 5. Access Web UI: http://tower-of-joy:11500 or http://audio.schweitz.internal (via NPM)
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#
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# NPM Configuration (audio.schweitz.internal):
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# - Domain: audio.schweitz.internal
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# - Scheme: http
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# - Forward Hostname: stable-audio (or tower-of-joy)
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# - Forward Port: 11500
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# - Block Common Exploits: Yes
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# - Websockets Support: Yes (required for Gradio)
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#
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# API Usage (Gradio API):
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# curl -X POST http://audio.schweitz.internal/api/predict \
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# -H "Content-Type: application/json" \
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# -d '{"data": ["epic orchestral music, cinematic", 30, 100, 7]}'
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# Parameters: [prompt, duration_seconds, diffusion_steps, cfg_scale]
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#
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# Rebuilding Image:
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# cd /home/jpmschweitzer/docker-data/stable-audio-open
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# git pull
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# docker build -t stable-audio-open:local .
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version: '3.8'
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# TRELLIS 1 - 3D Model Generation (Low-VRAM Fork)
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# Purpose: Generate 3D meshes with UV mappings for game asset pipeline
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# Ports: 11510 (Gradio Web UI)
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# GPU: YES - Optimized for 11GB VRAM (RTX 2080 Ti)
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# Storage: SSD for model cache, HDD for GLB outputs
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# Image: Built locally from /home/jpmschweitzer/docker-data/trellis/
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services:
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trellis:
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image: trellis:local
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container_name: trellis
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restart: unless-stopped
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ports:
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- "11510:7860" # Gradio Web UI
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volumes:
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# SSD: HuggingFace model cache (~5GB)
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- /home/jpmschweitzer/docker-data/trellis/hf-cache:/root/.cache/huggingface
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# HDD: Generated GLB files
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- /mnt/media/trellis/outputs:/app/outputs
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# Entrypoint script (patches Gradio bugs without rebuilding image)
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- /home/jpmschweitzer/docker-data/trellis/entrypoint.sh:/app/entrypoint.sh:ro
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command: ["bash", "/app/entrypoint.sh"]
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environment:
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- TZ=Europe/Amsterdam
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility
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# VRAM Optimization (for 11GB card)
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- ATTN_BACKEND=xformers
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- SPCONV_ALGO=native
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- PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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# Gradio must bind to 0.0.0.0 inside Docker
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- GRADIO_SERVER_NAME=0.0.0.0
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# HuggingFace token for model downloads (set in Portainer)
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- HF_TOKEN=${HF_TOKEN:-}
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healthcheck:
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test: ["CMD-SHELL", "curl -fSs http://localhost:7860/ || exit 1"]
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interval: 60s
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timeout: 30s
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retries: 3
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start_period: 300s # Model download + load time
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memswap_limit: 20G
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deploy:
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resources:
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limits:
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memory: 20G
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reservations:
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memory: 4G
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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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labels:
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- "com.centurylinklabs.watchtower.enable=false" # Manual updates for local builds
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networks:
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- docker-dataplane
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networks:
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docker-dataplane:
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external: true
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name: docker-dataplane
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# =============================================================================
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# PREREQUISITES
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# =============================================================================
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#
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# 1. Build the Docker image first:
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# cd /home/jpmschweitzer/docker-data/trellis
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# docker build -t trellis:local .
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# (Takes ~20-30 minutes for CUDA compilation)
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#
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# 2. Directories are already created:
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# /home/jpmschweitzer/docker-data/trellis/hf-cache (SSD - model cache)
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# /mnt/media/trellis/outputs (HDD - generated GLB files)
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#
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# =============================================================================
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# AFTER DEPLOYMENT
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# =============================================================================
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#
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# 1. Verify GPU access:
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# docker exec trellis nvidia-smi
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#
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# 2. Check logs:
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# docker logs trellis
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#
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# 3. Access Web UI:
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# http://tower-of-joy:11510
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# http://192.168.86.149:11510
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#
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# 4. Monitor VRAM during generation:
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# watch -n 1 nvidia-smi
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#
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# =============================================================================
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# USAGE - GAME ASSET PIPELINE
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# =============================================================================
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#
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# 1. Open Gradio UI at http://tower-of-joy:11510
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# 2. Upload reference image or enter text prompt
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# 3. Generate 3D model (uses 6-8GB VRAM)
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# 4. Download GLB file from outputs
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# 5. Import GLB into Blender (File > Import > glTF)
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# 6. Apply custom textures, set up isometric camera
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# 7. Render at 1024x1024, downsample to 64x64 tile sprites
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#
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# =============================================================================
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# GPU CONTENTION NOTE
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# =============================================================================
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#
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# TRELLIS shares the RTX 2080 Ti with Ollama and Jellyfin.
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# For best results during complex generations:
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# docker stop ollama
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# # Run TRELLIS generation
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# docker start ollama
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#
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# =============================================================================
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