Sebastian Krüger
571431955d
- 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
27 lines
746 B
Docker
27 lines
746 B
Docker
# RunPod AI Orchestrator Template
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# Minimal Docker image for ComfyUI + vLLM orchestration
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# Models and application code live on network volume at /workspace
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FROM runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04
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# Install Supervisor for process management
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RUN pip install --no-cache-dir supervisor
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# Install Tailscale for VPN connectivity
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RUN curl -fsSL https://tailscale.com/install.sh | sh
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# Install additional system utilities
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RUN apt-get update && apt-get install -y \
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wget \
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&& rm -rf /var/lib/apt/lists/*
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# Copy the startup script
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COPY start.sh /start.sh
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RUN chmod +x /start.sh
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# Set working directory to /workspace (network volume mount point)
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WORKDIR /workspace
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# RunPod calls /start.sh by default
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CMD ["/start.sh"]
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