feat(stack): consolidate Ollama, Stable Audio, and TRELLIS into models stack
Merge three individual GPU service stacks into a unified models.yml. All services share the RTX 2080 Ti and docker-dataplane network. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.6
@@ -0,0 +1,199 @@
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version: '3.8'
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# Models Stack - GPU-Accelerated ML/AI Model Services
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# Purpose: All GPU model serving (LLM inference, audio generation, 3D generation)
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# Ports: 11434 (Ollama API), 11500 (Stable Audio UI), 11510 (TRELLIS UI)
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# GPU: YES - Shared RTX 2080 Ti (11GB VRAM)
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# Network: docker-dataplane
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services:
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# ============================================
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# Ollama - LLM Inference Server
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# API: http://localhost:11434
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# ============================================
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ollama:
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image: ollama/ollama:latest
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container_name: ollama
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restart: unless-stopped
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ports:
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- "11434:11434"
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volumes:
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# Model storage on SSD for fast load times
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- /home/jpmschweitzer/docker-data/ollama/models:/root/.ollama
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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=all
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# Process requests sequentially to avoid batch overflow panics
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- OLLAMA_NUM_PARALLEL=1
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# Unload LLMs after 5 minutes idle (keeps VRAM free for other services)
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- OLLAMA_KEEP_ALIVE=5m
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# Only keep one model loaded at a time (embedding model stays, LLMs swap)
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- OLLAMA_MAX_LOADED_MODELS=1
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healthcheck:
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test: ["CMD-SHELL", "curl -fSs http://localhost:11434/api/tags || exit 1"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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deploy:
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resources:
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limits:
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memory: 8G
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reservations:
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memory: 1G
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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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# ============================================
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# Stable Audio Open - AI Audio Generation
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# Web UI: http://localhost:11500
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# Image: locally built
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# ============================================
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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"
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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
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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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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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networks:
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- docker-dataplane
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# ============================================
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# TRELLIS - 3D Model Generation (Low-VRAM Fork)
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# Web UI: http://localhost:11510
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# Image: locally built
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# ============================================
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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"
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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
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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"
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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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# GPU CONTENTION
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# =============================================================================
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#
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# All three services share the RTX 2080 Ti (11GB VRAM).
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# Ollama unloads models after 5 min idle to free VRAM.
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# For heavy Stable Audio or TRELLIS generations, consider stopping Ollama:
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# docker stop ollama && <generate> && docker start ollama
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#
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# =============================================================================
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# OLLAMA
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# =============================================================================
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#
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# After Deployment:
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# 1. Verify GPU access: docker exec ollama nvidia-smi
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# 2. Pull a model: docker exec ollama ollama pull llama3.2:3b
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# 3. List models: docker exec ollama ollama list
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# 4. Test inference: docker exec ollama ollama run llama3.2:3b "Hello"
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#
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# Recommended Models for RTX 2080 Ti (11GB VRAM):
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# - llama3.2:3b (2GB) - Fast, general purpose
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# - mistral:7b (4GB) - High quality, coding
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# - codellama:7b (4GB) - Code-specialized
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# - phi3:mini (2GB) - Fast reasoning
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#
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# =============================================================================
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# STABLE AUDIO
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# =============================================================================
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#
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# Prerequisites:
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# 1. Build image: cd ~/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. mkdir -p ~/docker-data/stable-audio/hf-cache
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# 5. Set HF_TOKEN in Portainer environment variables
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#
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# =============================================================================
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# TRELLIS
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# =============================================================================
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#
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# Prerequisites:
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# 1. Build image: cd ~/docker-data/trellis && docker build -t trellis:local . (~20-30 min)
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# 2. mkdir -p ~/docker-data/trellis/hf-cache /mnt/media/trellis/outputs
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#
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# Game Asset Pipeline:
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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 into Blender, apply textures, render sprites
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@@ -1,78 +0,0 @@
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version: '3.8'
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# Ollama - GPU-Accelerated ML Model Serving
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# Phase 1: Foundation Setup
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# Ports: 11434 (API)
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# GPU: YES - Requires NVIDIA Container Toolkit
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# Storage: SSD or HDD for models (models are 2-15GB each)
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services:
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ollama:
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image: ollama/ollama:latest
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container_name: ollama
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restart: unless-stopped
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ports:
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- "11434:11434" # Ollama API endpoint
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volumes:
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# Model storage - choose based on available space:
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# SSD (faster load times): /home/jpmschweitzer/docker-data/ollama/models
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# HDD (more space): /mnt/media/ollama/models
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- /home/jpmschweitzer/docker-data/ollama/models:/root/.ollama
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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=all
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# Process requests sequentially to avoid batch overflow panics
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- OLLAMA_NUM_PARALLEL=1
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# Unload LLMs after 5 minutes idle (keeps VRAM free for other services)
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- OLLAMA_KEEP_ALIVE=5m
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# Only keep one model loaded at a time (embedding model stays, LLMs swap)
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- OLLAMA_MAX_LOADED_MODELS=1
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healthcheck:
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test: ["CMD-SHELL", "curl -fSs http://localhost:11434/api/tags || exit 1"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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deploy:
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resources:
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limits:
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memory: 8G
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reservations:
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memory: 1G
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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)
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# - Suitable for 3B-13B parameter models
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# - NVIDIA Container Toolkit must be installed
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#
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# After Deployment:
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# 1. Verify GPU access: docker exec ollama nvidia-smi
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# 2. Pull a model: docker exec ollama ollama pull llama3.2:3b
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# 3. List models: docker exec ollama ollama list
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# 4. Test inference: docker exec ollama ollama run llama3.2:3b "Hello"
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# 5. Monitor GPU during inference: watch -n 1 nvidia-smi
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#
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# Recommended Models for RTX 2080 Ti (11GB VRAM):
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# - llama3.2:3b (2GB) - Fast, general purpose
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# - mistral:7b (4GB) - High quality, coding
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# - codellama:7b (4GB) - Code-specialized
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# - phi3:mini (2GB) - Fast reasoning
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#
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# API Usage:
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# curl http://localhost:11434/api/generate -d '{
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# "model": "llama3.2:3b",
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# "prompt": "Why is the sky blue?",
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# "stream": false
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# }'
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@@ -1,90 +0,0 @@
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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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@@ -1,116 +0,0 @@
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version: '3.8'
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|
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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)
|
||||
# Storage: SSD for model cache, HDD for GLB outputs
|
||||
# Image: Built locally from /home/jpmschweitzer/docker-data/trellis/
|
||||
|
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services:
|
||||
trellis:
|
||||
image: trellis:local
|
||||
container_name: trellis
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "11510:7860" # Gradio Web UI
|
||||
volumes:
|
||||
# SSD: HuggingFace model cache (~5GB)
|
||||
- /home/jpmschweitzer/docker-data/trellis/hf-cache:/root/.cache/huggingface
|
||||
# HDD: Generated GLB files
|
||||
- /mnt/media/trellis/outputs:/app/outputs
|
||||
# Entrypoint script (patches Gradio bugs without rebuilding image)
|
||||
- /home/jpmschweitzer/docker-data/trellis/entrypoint.sh:/app/entrypoint.sh:ro
|
||||
command: ["bash", "/app/entrypoint.sh"]
|
||||
environment:
|
||||
- TZ=Europe/Amsterdam
|
||||
- NVIDIA_VISIBLE_DEVICES=all
|
||||
- NVIDIA_DRIVER_CAPABILITIES=compute,utility
|
||||
# VRAM Optimization (for 11GB card)
|
||||
- ATTN_BACKEND=xformers
|
||||
- SPCONV_ALGO=native
|
||||
- PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
||||
# Gradio must bind to 0.0.0.0 inside Docker
|
||||
- GRADIO_SERVER_NAME=0.0.0.0
|
||||
# HuggingFace token for model downloads (set in Portainer)
|
||||
- HF_TOKEN=${HF_TOKEN:-}
|
||||
healthcheck:
|
||||
test: ["CMD-SHELL", "curl -fSs http://localhost:7860/ || exit 1"]
|
||||
interval: 60s
|
||||
timeout: 30s
|
||||
retries: 3
|
||||
start_period: 300s # Model download + load time
|
||||
memswap_limit: 20G
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
memory: 20G
|
||||
reservations:
|
||||
memory: 4G
|
||||
devices:
|
||||
- driver: nvidia
|
||||
count: 1
|
||||
capabilities: [gpu]
|
||||
labels:
|
||||
- "com.centurylinklabs.watchtower.enable=false" # Manual updates for local builds
|
||||
networks:
|
||||
- docker-dataplane
|
||||
|
||||
networks:
|
||||
docker-dataplane:
|
||||
external: true
|
||||
name: docker-dataplane
|
||||
|
||||
# =============================================================================
|
||||
# PREREQUISITES
|
||||
# =============================================================================
|
||||
#
|
||||
# 1. Build the Docker image first:
|
||||
# cd /home/jpmschweitzer/docker-data/trellis
|
||||
# docker build -t trellis:local .
|
||||
# (Takes ~20-30 minutes for CUDA compilation)
|
||||
#
|
||||
# 2. Directories are already created:
|
||||
# /home/jpmschweitzer/docker-data/trellis/hf-cache (SSD - model cache)
|
||||
# /mnt/media/trellis/outputs (HDD - generated GLB files)
|
||||
#
|
||||
# =============================================================================
|
||||
# AFTER DEPLOYMENT
|
||||
# =============================================================================
|
||||
#
|
||||
# 1. Verify GPU access:
|
||||
# docker exec trellis nvidia-smi
|
||||
#
|
||||
# 2. Check logs:
|
||||
# docker logs trellis
|
||||
#
|
||||
# 3. Access Web UI:
|
||||
# http://tower-of-joy:11510
|
||||
# http://192.168.86.149:11510
|
||||
#
|
||||
# 4. Monitor VRAM during generation:
|
||||
# watch -n 1 nvidia-smi
|
||||
#
|
||||
# =============================================================================
|
||||
# USAGE - GAME ASSET PIPELINE
|
||||
# =============================================================================
|
||||
#
|
||||
# 1. Open Gradio UI at http://tower-of-joy:11510
|
||||
# 2. Upload reference image or enter text prompt
|
||||
# 3. Generate 3D model (uses 6-8GB VRAM)
|
||||
# 4. Download GLB file from outputs
|
||||
# 5. Import GLB into Blender (File > Import > glTF)
|
||||
# 6. Apply custom textures, set up isometric camera
|
||||
# 7. Render at 1024x1024, downsample to 64x64 tile sprites
|
||||
#
|
||||
# =============================================================================
|
||||
# GPU CONTENTION NOTE
|
||||
# =============================================================================
|
||||
#
|
||||
# TRELLIS shares the RTX 2080 Ti with Ollama and Jellyfin.
|
||||
# For best results during complex generations:
|
||||
# docker stop ollama
|
||||
# # Run TRELLIS generation
|
||||
# docker start ollama
|
||||
#
|
||||
# =============================================================================
|
||||
Reference in New Issue
Block a user