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>
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models/musicgen/server.py
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172
models/musicgen/server.py
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#!/usr/bin/env python3
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"""
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MusicGen Music Generation Service
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OpenAI-compatible music generation using Meta's MusicGen Medium model.
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Provides /v1/audio/generations endpoint.
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"""
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import base64
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import io
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import os
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import tempfile
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from typing import Optional
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import torch
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import torchaudio
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from audiocraft.models import MusicGen
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from fastapi import HTTPException
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from pydantic import BaseModel, Field
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# Import base service class
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))
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from core.base_service import GPUService
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class AudioGenerationRequest(BaseModel):
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"""Music generation request"""
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model: str = Field(default="musicgen-medium", description="Model name")
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prompt: str = Field(..., description="Text description of the music to generate")
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duration: float = Field(default=30.0, ge=1.0, le=30.0, description="Duration in seconds")
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temperature: float = Field(default=1.0, ge=0.1, le=2.0, description="Sampling temperature")
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top_k: int = Field(default=250, ge=0, le=500, description="Top-k sampling")
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top_p: float = Field(default=0.0, ge=0.0, le=1.0, description="Top-p (nucleus) sampling")
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cfg_coef: float = Field(default=3.0, ge=1.0, le=15.0, description="Classifier-free guidance coefficient")
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response_format: str = Field(default="wav", description="Audio format (wav or mp3)")
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class AudioGenerationResponse(BaseModel):
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"""Music generation response"""
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audio: str = Field(..., description="Base64-encoded audio data")
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format: str = Field(..., description="Audio format (wav or mp3)")
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duration: float = Field(..., description="Duration in seconds")
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sample_rate: int = Field(..., description="Sample rate in Hz")
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class MusicGenService(GPUService):
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"""MusicGen music generation service"""
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def __init__(self):
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# Get port from environment or use default
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port = int(os.getenv("PORT", "8003"))
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super().__init__(name="musicgen-medium", port=port)
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# Service-specific attributes
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self.model: Optional[MusicGen] = None
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self.model_name = os.getenv("MODEL_NAME", "facebook/musicgen-medium")
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async def initialize(self):
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"""Initialize MusicGen model"""
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await super().initialize()
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self.logger.info(f"Loading MusicGen model: {self.model_name}")
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# Load model
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device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model = MusicGen.get_pretrained(self.model_name, device=device)
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self.logger.info(f"MusicGen model loaded successfully")
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self.logger.info(f"Max duration: 30 seconds at {self.model.sample_rate}Hz")
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async def cleanup(self):
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"""Cleanup resources"""
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await super().cleanup()
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if self.model:
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self.logger.info("MusicGen model cleanup")
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self.model = None
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def create_app(self):
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"""Create FastAPI routes"""
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@self.app.get("/")
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async def root():
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"""Root endpoint"""
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return {
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"service": "MusicGen API Server",
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"model": self.model_name,
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"max_duration": 30.0,
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"sample_rate": self.model.sample_rate if self.model else 32000
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}
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@self.app.get("/v1/models")
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async def list_models():
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"""List available models (OpenAI-compatible)"""
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return {
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"object": "list",
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"data": [
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{
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"id": "musicgen-medium",
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"object": "model",
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"created": 1234567890,
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"owned_by": "meta",
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"permission": [],
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"root": self.model_name,
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"parent": None,
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}
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]
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}
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@self.app.post("/v1/audio/generations")
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async def generate_audio(request: AudioGenerationRequest) -> AudioGenerationResponse:
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"""Generate music from text prompt"""
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if not self.model:
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raise HTTPException(status_code=503, detail="Model not initialized")
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self.logger.info(f"Generating music: {request.prompt[:100]}...")
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self.logger.info(f"Duration: {request.duration}s, Temperature: {request.temperature}")
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try:
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# Set generation parameters
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self.model.set_generation_params(
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duration=request.duration,
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temperature=request.temperature,
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top_k=request.top_k,
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top_p=request.top_p,
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cfg_coef=request.cfg_coef,
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)
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# Generate audio
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descriptions = [request.prompt]
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with torch.no_grad():
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wav = self.model.generate(descriptions)
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# wav shape: [batch_size, channels, samples]
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# Extract first batch item
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audio_data = wav[0].cpu() # [channels, samples]
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# Get sample rate
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sample_rate = self.model.sample_rate
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# Save to temporary file
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
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temp_path = temp_file.name
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torchaudio.save(temp_path, audio_data, sample_rate)
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# Read audio file and encode to base64
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with open(temp_path, 'rb') as f:
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audio_bytes = f.read()
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# Clean up temporary file
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os.unlink(temp_path)
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# Encode to base64
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audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
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self.logger.info(f"Generated {request.duration}s of audio")
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return AudioGenerationResponse(
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audio=audio_base64,
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format="wav",
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duration=request.duration,
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sample_rate=sample_rate
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)
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except Exception as e:
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self.logger.error(f"Error generating audio: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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service = MusicGenService()
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service.run()
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