feat: implement Ansible-based process architecture for RunPod

Major architecture overhaul to address RunPod Docker limitations:

Core Infrastructure:
- Add base_service.py: Abstract base class for all AI services
- Add service_manager.py: Process lifecycle management
- Add core/requirements.txt: Core dependencies

Model Services (Standalone Python):
- Add models/vllm/server.py: Qwen 2.5 7B text generation
- Add models/flux/server.py: Flux.1 Schnell image generation
- Add models/musicgen/server.py: MusicGen Medium music generation
- Each service inherits from GPUService base class
- OpenAI-compatible APIs
- Standalone execution support

Ansible Deployment:
- Add playbook.yml: Comprehensive deployment automation
- Add ansible.cfg: Ansible configuration
- Add inventory.yml: Localhost inventory
- Tags: base, python, dependencies, models, tailscale, validate, cleanup

Scripts:
- Add scripts/install.sh: Full installation wrapper
- Add scripts/download-models.sh: Model download wrapper
- Add scripts/start-all.sh: Start orchestrator
- Add scripts/stop-all.sh: Stop all services

Documentation:
- Update ARCHITECTURE.md: Document distributed VPS+GPU architecture

Benefits:
- No Docker: Avoids RunPod CAP_SYS_ADMIN limitations
- Fully reproducible via Ansible
- Extensible: Add models in 3 steps
- Direct Python execution (no container overhead)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

core/requirements.txt

@@ -0,0 +1,15 @@
+# Core dependencies for AI service infrastructure
+
+# FastAPI and server
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+pydantic==2.5.0
+
+# HTTP client for health checks and proxying
+httpx==0.25.1
+
+# YAML configuration
+pyyaml==6.0.1
+
+# Process management
+psutil==5.9.6