Initial commit: RunPod multi-modal AI orchestration stack
- Multi-modal AI infrastructure for RunPod RTX 4090 - Automatic model orchestration (text, image, music) - Text: vLLM + Qwen 2.5 7B Instruct - Image: Flux.1 Schnell via OpenEDAI - Music: MusicGen Medium via AudioCraft - Cost-optimized sequential loading on single GPU - Template preparation scripts for rapid deployment - Comprehensive documentation (README, DEPLOYMENT, TEMPLATE)
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gpu-server-compose.yaml
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237
gpu-server-compose.yaml
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# GPU Server Docker Compose Configuration
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# Deploy on RunPod GPU server (10.8.0.2)
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# Services accessible from VPS (10.8.0.1) via WireGuard VPN
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version: '3.8'
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services:
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# =============================================================================
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# vLLM - High-performance LLM Inference Server
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# =============================================================================
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vllm:
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image: vllm/vllm-openai:latest
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container_name: gpu_vllm
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restart: unless-stopped
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runtime: nvidia
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environment:
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NVIDIA_VISIBLE_DEVICES: all
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CUDA_VISIBLE_DEVICES: "0"
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HF_TOKEN: ${HF_TOKEN:-}
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volumes:
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- ${MODELS_PATH:-/workspace/models}:/root/.cache/huggingface
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command:
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- --model
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- meta-llama/Meta-Llama-3.1-8B-Instruct # Change model here
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- --host
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- 0.0.0.0
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- --port
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- 8000
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- --tensor-parallel-size
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- "1"
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- --gpu-memory-utilization
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- "0.85" # Leave 15% for other tasks
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- --max-model-len
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- "8192"
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- --dtype
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- auto
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- --trust-remote-code
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ports:
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- "8000:8000"
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 120s # Model loading takes time
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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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labels:
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- "service=vllm"
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- "stack=gpu-ai"
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# =============================================================================
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# ComfyUI - Advanced Stable Diffusion Interface
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# =============================================================================
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comfyui:
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image: ghcr.io/ai-dock/comfyui:latest
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container_name: gpu_comfyui
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restart: unless-stopped
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runtime: nvidia
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environment:
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NVIDIA_VISIBLE_DEVICES: all
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TZ: ${TIMEZONE:-Europe/Berlin}
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# ComfyUI auto-installs custom nodes on first run
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COMFYUI_FLAGS: "--listen 0.0.0.0 --port 8188"
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volumes:
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- comfyui_data:/data
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- ${MODELS_PATH:-/workspace/models}/comfyui:/opt/ComfyUI/models
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- comfyui_output:/opt/ComfyUI/output
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- comfyui_input:/opt/ComfyUI/input
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ports:
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- "8188:8188"
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8188/"]
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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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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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labels:
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- "service=comfyui"
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- "stack=gpu-ai"
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# =============================================================================
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# Axolotl - LLM Fine-tuning Framework
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# =============================================================================
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# Note: This service uses "profiles" - only starts when explicitly requested
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# Start with: docker compose --profile training up -d axolotl
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axolotl:
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image: winglian/axolotl:main-py3.11-cu121-2.2.2
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container_name: gpu_training
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runtime: nvidia
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volumes:
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- ./training/configs:/workspace/configs
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- ./training/data:/workspace/data
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- ./training/output:/workspace/output
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- ${MODELS_PATH:-/workspace/models}:/workspace/models
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- training_cache:/root/.cache
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environment:
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NVIDIA_VISIBLE_DEVICES: all
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WANDB_API_KEY: ${WANDB_API_KEY:-}
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HF_TOKEN: ${HF_TOKEN:-}
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working_dir: /workspace
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# Default command - override when running specific training
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command: sleep infinity
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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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profiles:
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- training
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labels:
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- "service=axolotl"
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- "stack=gpu-ai"
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# =============================================================================
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# JupyterLab - Interactive Development Environment
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# =============================================================================
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jupyter:
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image: pytorch/pytorch:2.3.0-cuda12.1-cudnn8-devel
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container_name: gpu_jupyter
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restart: unless-stopped
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runtime: nvidia
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volumes:
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- ./notebooks:/workspace/notebooks
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- ${MODELS_PATH:-/workspace/models}:/workspace/models
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- jupyter_cache:/root/.cache
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ports:
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- "8888:8888"
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environment:
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NVIDIA_VISIBLE_DEVICES: all
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JUPYTER_ENABLE_LAB: "yes"
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JUPYTER_TOKEN: ${JUPYTER_TOKEN:-pivoine-ai-2025}
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HF_TOKEN: ${HF_TOKEN:-}
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command: |
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bash -c "
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pip install --quiet jupyterlab transformers datasets accelerate bitsandbytes peft trl sentencepiece protobuf &&
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jupyter lab --ip=0.0.0.0 --port=8888 --allow-root --no-browser --NotebookApp.token='${JUPYTER_TOKEN:-pivoine-ai-2025}'
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"
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8888/"]
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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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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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labels:
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- "service=jupyter"
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- "stack=gpu-ai"
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# =============================================================================
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# Netdata - System & GPU Monitoring
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# =============================================================================
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netdata:
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image: netdata/netdata:latest
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container_name: gpu_netdata
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restart: unless-stopped
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runtime: nvidia
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hostname: gpu-runpod
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cap_add:
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- SYS_PTRACE
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- SYS_ADMIN
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security_opt:
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- apparmor:unconfined
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environment:
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NVIDIA_VISIBLE_DEVICES: all
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TZ: ${TIMEZONE:-Europe/Berlin}
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volumes:
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- /sys:/host/sys:ro
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- /proc:/host/proc:ro
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- /var/run/docker.sock:/var/run/docker.sock:ro
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- /etc/os-release:/host/etc/os-release:ro
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- netdata_config:/etc/netdata
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- netdata_cache:/var/cache/netdata
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- netdata_lib:/var/lib/netdata
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ports:
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- "19999:19999"
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labels:
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- "service=netdata"
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- "stack=gpu-ai"
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# =============================================================================
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# Volumes
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# =============================================================================
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volumes:
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# ComfyUI data
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comfyui_data:
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driver: local
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comfyui_output:
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driver: local
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comfyui_input:
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driver: local
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# Training data
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training_cache:
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driver: local
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# Jupyter data
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jupyter_cache:
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driver: local
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# Netdata data
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netdata_config:
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driver: local
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netdata_cache:
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driver: local
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netdata_lib:
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driver: local
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# =============================================================================
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# Networks
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# =============================================================================
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networks:
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default:
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driver: bridge
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ipam:
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config:
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- subnet: 172.25.0.0/24
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