feat: add RunPod Docker template with automated build workflow

- Add Dockerfile with minimal setup (supervisor, tailscale)
- Add start.sh bootstrap script for container initialization
- Add Gitea workflow for automated Docker image builds
- Add comprehensive RUNPOD_TEMPLATE.md documentation
- Add bootstrap-venvs.sh for Python venv health checks

This enables deployment of the AI orchestrator on RunPod using:
- Minimal Docker image (~2-3GB) for fast deployment
- Network volume for models and data persistence (~80-200GB)
- Automated builds on push to main or version tags
- Full Tailscale VPN integration
- Supervisor process management

Dockerfile

@@ -0,0 +1,26 @@
+# RunPod AI Orchestrator Template
+# Minimal Docker image for ComfyUI + vLLM orchestration
+# Models and application code live on network volume at /workspace
+
+FROM runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04
+
+# Install Supervisor for process management
+RUN pip install --no-cache-dir supervisor
+
+# Install Tailscale for VPN connectivity
+RUN curl -fsSL https://tailscale.com/install.sh | sh
+
+# Install additional system utilities
+RUN apt-get update && apt-get install -y \
+    wget \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy the startup script
+COPY start.sh /start.sh
+RUN chmod +x /start.sh
+
+# Set working directory to /workspace (network volume mount point)
+WORKDIR /workspace
+
+# RunPod calls /start.sh by default
+CMD ["/start.sh"]