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504 lines
12 KiB
Markdown
504 lines
12 KiB
Markdown
# RunPod Template Setup Guide
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This guide explains how to deploy the AI Orchestrator (ComfyUI + vLLM) on RunPod using a custom Docker template and network volume.
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## Architecture Overview
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The deployment uses a **two-tier strategy**:
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1. **Docker Image** (software layer) - Contains system packages, Supervisor, Tailscale
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2. **Network Volume** (data layer) - Contains models, ComfyUI installation, venvs, configuration
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This approach allows fast pod deployment (~2-3 minutes) while keeping all large files (models, ~80-200GB) on a persistent network volume.
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## Prerequisites
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- RunPod account with credits
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- Docker Hub account (for hosting the template image)
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- HuggingFace account with API token (for model downloads)
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- Tailscale account with auth key (optional, for VPN access)
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## Step 1: Build and Push Docker Image
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### Option A: Automated Build (Recommended)
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The repository includes a Gitea workflow that automatically builds and pushes the Docker image to your Gitea container registry when you push to the `main` branch or create a version tag.
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1. **Configure Gitea Secret:**
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- Go to your Gitea repository → Settings → Secrets
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- Add `REGISTRY_TOKEN` = your Gitea access token with registry permissions
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- (The workflow automatically uses your Gitea username via `gitea.actor`)
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2. **Trigger Build:**
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```bash
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# Push to main branch
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git push origin main
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# Or create a version tag
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git tag v1.0.0
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git push origin v1.0.0
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```
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3. **Monitor Build:**
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- Go to Actions tab in Gitea
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- Wait for build to complete (~5-10 minutes)
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- Note the Docker image name: `dev.pivoine.art/valknar/runpod-ai-orchestrator:latest`
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### Option B: Manual Build
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If you prefer to build manually:
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```bash
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# From the repository root
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cd /path/to/runpod
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# Build the image
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docker build -t dev.pivoine.art/valknar/runpod-ai-orchestrator:latest .
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# Login to your Gitea registry
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docker login dev.pivoine.art
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# Push to Gitea registry
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docker push dev.pivoine.art/valknar/runpod-ai-orchestrator:latest
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```
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## Step 2: Create Network Volume
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Network volumes persist your models and data across pod restarts and rebuilds.
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1. **Go to RunPod Dashboard → Storage → Network Volumes**
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2. **Click "New Network Volume"**
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3. **Configure:**
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- **Name**: `ai-orchestrator-models`
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- **Size**: `200GB` (adjust based on your needs)
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- Essential models only: ~80GB
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- All models: ~137-200GB
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- **Datacenter**: Choose closest to you (volume tied to datacenter)
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4. **Click "Create Volume"**
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5. **Note the Volume ID** (e.g., `vol-abc123def456`) for pod deployment
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### Storage Requirements
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| Configuration | Size | Models Included |
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|--------------|------|-----------------|
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| Essential | ~80GB | FLUX Schnell, 1-2 SDXL checkpoints, MusicGen Medium |
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| Complete | ~137GB | All image/video/audio models from playbook |
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| Full + vLLM | ~200GB | Complete + Qwen 2.5 7B + Llama 3.1 8B |
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## Step 3: Create RunPod Template
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1. **Go to RunPod Dashboard → Templates**
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2. **Click "New Template"**
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3. **Configure Template Settings:**
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**Container Configuration:**
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- **Template Name**: `AI Orchestrator (ComfyUI + vLLM)`
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- **Template Type**: Docker
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- **Container Image**: `dev.pivoine.art/valknar/runpod-ai-orchestrator:latest`
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- **Container Disk**: `50GB` (for system and temp files)
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- **Docker Command**: Leave empty (uses default `/start.sh`)
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**Volume Configuration:**
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- **Volume Mount Path**: `/workspace`
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- **Attach to Network Volume**: Select your volume ID from Step 2
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**Port Configuration:**
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- **Expose HTTP Ports**: `8188, 9000, 9001`
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- `8188` - ComfyUI web interface
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- `9000` - Model orchestrator API
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- `9001` - Supervisor web UI
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- **Expose TCP Ports**: `22` (SSH access)
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**Environment Variables:**
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```
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HF_TOKEN=your_huggingface_token_here
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TAILSCALE_AUTHKEY=tskey-auth-your_tailscale_authkey_here
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SUPERVISOR_BACKEND_HOST=localhost
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SUPERVISOR_BACKEND_PORT=9001
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```
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**Advanced Settings:**
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- **Start Jupyter**: No
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- **Start SSH**: Yes (handled by base image)
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4. **Click "Save Template"**
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## Step 4: First Deployment (Initial Setup)
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The first time you deploy, you need to set up the network volume with models and configuration.
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### 4.1 Deploy Pod
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1. **Go to RunPod Dashboard → Pods**
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2. **Click "Deploy"** or "GPU Pods"
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3. **Select your custom template**: `AI Orchestrator (ComfyUI + vLLM)`
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4. **Configure GPU:**
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- **GPU Type**: RTX 4090 (24GB VRAM) or higher
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- **Network Volume**: Select your volume from Step 2
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- **On-Demand vs Spot**: Choose based on budget
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5. **Click "Deploy"**
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### 4.2 SSH into Pod
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```bash
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# Get pod SSH command from RunPod dashboard
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ssh root@<pod-ip> -p <port> -i ~/.ssh/id_ed25519
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# Or use RunPod web terminal
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```
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### 4.3 Initial Setup on Network Volume
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```bash
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# 1. Clone the repository to /workspace/ai
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cd /workspace
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git clone https://github.com/your-username/runpod.git ai
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cd ai
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# 2. Create .env file with your credentials
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cp .env.example .env
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nano .env
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# Edit and add:
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# HF_TOKEN=your_huggingface_token
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# TAILSCALE_AUTHKEY=tskey-auth-your_key
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# GPU_TAILSCALE_IP=<will be set automatically>
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# 3. Download essential models (this takes 30-60 minutes)
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ansible-playbook playbook.yml --tags comfyui-essential
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# OR download all models (1-2 hours)
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ansible-playbook playbook.yml --tags comfyui-models-all
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# 4. Link models to ComfyUI
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bash scripts/link-comfyui-models.sh
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# OR if arty is available
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arty run models/link-comfyui
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# 5. Install ComfyUI custom nodes dependencies
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cd /workspace/ComfyUI/custom_nodes/ComfyUI-Manager
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pip install -r requirements.txt
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cd /workspace/ai
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# 6. Restart the container to apply all changes
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exit
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# Go to RunPod dashboard → Stop pod → Start pod
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```
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### 4.4 Verify Services
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After restart, SSH back in and check:
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```bash
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# Check supervisor status
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supervisorctl -c /workspace/supervisord.conf status
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# Expected output:
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# comfyui RUNNING pid 123, uptime 0:01:00
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# (orchestrator is disabled by default - enable for vLLM)
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# Test ComfyUI
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curl -I http://localhost:8188
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# Test Supervisor web UI
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curl -I http://localhost:9001
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```
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## Step 5: Subsequent Deployments
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After initial setup, deploying new pods is quick (2-3 minutes):
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1. **Deploy pod** with same template + network volume
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2. **Wait for startup** (~1-2 minutes for services to start)
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3. **Access services:**
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- ComfyUI: `http://<pod-ip>:8188`
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- Supervisor: `http://<pod-ip>:9001`
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**All models, configuration, and data persist on the network volume!**
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## Step 6: Access Services
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### Via Direct IP (HTTP)
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Get pod IP and ports from RunPod dashboard:
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```
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ComfyUI: http://<pod-ip>:8188
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Supervisor UI: http://<pod-ip>:9001
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Orchestrator API: http://<pod-ip>:9000
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SSH: ssh root@<pod-ip> -p <port>
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```
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### Via Tailscale VPN (Recommended)
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If you configured `TAILSCALE_AUTHKEY`, the pod automatically joins your Tailscale network:
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1. **Get Tailscale IP:**
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```bash
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ssh root@<pod-ip> -p <port>
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tailscale ip -4
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# Example output: 100.114.60.40
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```
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2. **Access via Tailscale:**
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```
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ComfyUI: http://<tailscale-ip>:8188
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Supervisor: http://<tailscale-ip>:9001
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Orchestrator: http://<tailscale-ip>:9000
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SSH: ssh root@<tailscale-ip>
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```
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3. **Update LiteLLM config** on your VPS with the Tailscale IP
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## Service Management
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### Start/Stop Services
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```bash
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# Start all services
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supervisorctl -c /workspace/supervisord.conf start all
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# Stop all services
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supervisorctl -c /workspace/supervisord.conf stop all
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# Restart specific service
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supervisorctl -c /workspace/supervisord.conf restart comfyui
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# View status
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supervisorctl -c /workspace/supervisord.conf status
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```
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### Enable vLLM Models (Text Generation)
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By default, only ComfyUI runs (to save VRAM). To enable vLLM:
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1. **Stop ComfyUI** (frees up VRAM):
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```bash
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supervisorctl -c /workspace/supervisord.conf stop comfyui
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```
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2. **Start orchestrator** (manages vLLM models):
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```bash
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supervisorctl -c /workspace/supervisord.conf start orchestrator
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```
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3. **Test text generation:**
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```bash
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curl -X POST http://localhost:9000/v1/chat/completions \
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-H 'Content-Type: application/json' \
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-d '{"model":"qwen-2.5-7b","messages":[{"role":"user","content":"Hello"}]}'
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```
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### Switch Back to ComfyUI
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```bash
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# Stop orchestrator (stops all vLLM models)
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supervisorctl -c /workspace/supervisord.conf stop orchestrator
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# Start ComfyUI
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supervisorctl -c /workspace/supervisord.conf start comfyui
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```
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## Updating the Template
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When you make changes to code or configuration:
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### Update Docker Image
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```bash
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# 1. Make changes to Dockerfile or start.sh
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# 2. Push to repository
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git add .
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git commit -m "Update template configuration"
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git push origin main
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# 3. Gitea workflow auto-builds new image
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# 4. Terminate old pod and deploy new one with updated image
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```
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### Update Network Volume Data
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```bash
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# SSH into running pod
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ssh root@<pod-ip> -p <port>
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# Update repository
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cd /workspace/ai
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git pull
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# Re-run Ansible if needed
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ansible-playbook playbook.yml --tags <specific-tag>
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# Restart services
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supervisorctl -c /workspace/supervisord.conf restart all
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```
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## Troubleshooting
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### Pod fails to start
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**Check logs:**
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```bash
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# Via SSH
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cat /workspace/logs/supervisord.log
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cat /workspace/logs/comfyui.err.log
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# Via RunPod web terminal
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tail -f /workspace/logs/*.log
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```
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**Common issues:**
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- Missing `.env` file → Create `/workspace/ai/.env` with required vars
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- Supervisor config not found → Ensure `/workspace/ai/supervisord.conf` exists
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- Port conflicts → Check if services are already running
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### Tailscale not connecting
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**Check Tailscale status:**
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```bash
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tailscale status
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tailscale ip -4
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```
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**Common issues:**
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- Missing or invalid `TAILSCALE_AUTHKEY` in `.env`
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- Auth key expired → Generate new key in Tailscale admin
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- Firewall blocking → RunPod should allow Tailscale by default
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### Services not starting
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**Check Supervisor:**
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```bash
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supervisorctl -c /workspace/supervisord.conf status
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supervisorctl -c /workspace/supervisord.conf tail -f comfyui
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```
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**Common issues:**
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- venv broken → Re-run `scripts/bootstrap-venvs.sh`
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- Models not downloaded → Run Ansible playbook again
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- Python version mismatch → Rebuild venvs
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### Out of VRAM
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**Check GPU memory:**
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```bash
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nvidia-smi
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```
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**RTX 4090 (24GB) capacity:**
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- ComfyUI (FLUX Schnell): ~23GB (can't run with vLLM)
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- vLLM (Qwen 2.5 7B): ~14GB
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- vLLM (Llama 3.1 8B): ~17GB
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**Solution:** Only run one service at a time (see Service Management section)
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### Network volume full
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**Check disk usage:**
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```bash
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df -h /workspace
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du -sh /workspace/*
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```
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**Clean up:**
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```bash
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# Remove old HuggingFace cache
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rm -rf /workspace/huggingface_cache
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# Re-download essential models only
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cd /workspace/ai
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ansible-playbook playbook.yml --tags comfyui-essential
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```
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## Cost Optimization
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### Spot vs On-Demand
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- **Spot instances**: ~70% cheaper, can be interrupted
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- **On-Demand**: More expensive, guaranteed availability
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**Recommendation:** Use spot for development, on-demand for production
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### Network Volume Pricing
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- First 1TB: $0.07/GB/month
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- Beyond 1TB: $0.05/GB/month
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**200GB volume cost:** ~$14/month
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### Pod Auto-Stop
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Configure auto-stop in RunPod pod settings to save costs when idle:
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- Stop after 15 minutes idle
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- Stop after 1 hour idle
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- Manual stop only
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## Advanced Configuration
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### Custom Environment Variables
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Add to template or pod environment variables:
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```bash
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# Model cache locations
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HF_HOME=/workspace/huggingface_cache
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TRANSFORMERS_CACHE=/workspace/huggingface_cache
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# ComfyUI settings
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COMFYUI_PORT=8188
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COMFYUI_LISTEN=0.0.0.0
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# Orchestrator settings
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ORCHESTRATOR_PORT=9000
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# GPU settings
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CUDA_VISIBLE_DEVICES=0
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```
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### Multiple Network Volumes
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You can attach multiple network volumes for organization:
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1. **Models volume** - `/workspace/models` (read-only, shared)
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2. **Data volume** - `/workspace/data` (read-write, per-project)
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### Custom Startup Script
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Override `/start.sh` behavior by creating `/workspace/custom-start.sh`:
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```bash
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#!/bin/bash
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# Custom startup commands
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# Source default startup
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source /start.sh
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# Add your custom commands here
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echo "Running custom initialization..."
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```
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## References
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- [RunPod Documentation](https://docs.runpod.io/)
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- [RunPod Templates Overview](https://docs.runpod.io/pods/templates/overview)
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- [Network Volumes Guide](https://docs.runpod.io/storage/network-volumes)
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- [ComfyUI Documentation](https://github.com/comfyanonymous/ComfyUI)
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- [Supervisor Documentation](http://supervisord.org/)
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- [Tailscale Documentation](https://tailscale.com/kb/)
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## Support
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For issues or questions:
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- Check troubleshooting section above
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- Review `/workspace/logs/` files
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- Check RunPod community forums
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- Open issue in project repository
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