Initial commit: RunPod multi-modal AI orchestration stack

- Multi-modal AI infrastructure for RunPod RTX 4090
- Automatic model orchestration (text, image, music)
- Text: vLLM + Qwen 2.5 7B Instruct
- Image: Flux.1 Schnell via OpenEDAI
- Music: MusicGen Medium via AudioCraft
- Cost-optimized sequential loading on single GPU
- Template preparation scripts for rapid deployment
- Comprehensive documentation (README, DEPLOYMENT, TEMPLATE)

musicgen/Dockerfile

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+FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
+
+WORKDIR /app
+
+# Install Python and system dependencies
+RUN apt-get update && apt-get install -y \
+    python3.10 \
+    python3-pip \
+    ffmpeg \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Upgrade pip
+RUN pip3 install --no-cache-dir --upgrade pip
+
+# Install PyTorch with CUDA support
+RUN pip3 install --no-cache-dir torch==2.1.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY server.py .
+
+# Create directory for model cache
+RUN mkdir -p /app/models
+
+# Environment variables
+ENV HF_HOME=/app/models
+ENV TORCH_HOME=/app/models
+ENV MODEL_NAME=facebook/musicgen-medium
+
+# Expose port
+EXPOSE 8000
+
+# Run the server
+CMD ["python3", "server.py"]

musicgen/requirements.txt

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+torch==2.1.0
+torchaudio==2.1.0
+audiocraft==1.3.0
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+pydantic==2.5.0

musicgen/server.py

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+#!/usr/bin/env python3
+"""
+MusicGen API Server
+OpenAI-compatible API for music generation using Meta's MusicGen
+
+Endpoints:
+- POST /v1/audio/generations - Generate music from text prompt
+- GET /health - Health check
+- GET / - Service info
+"""
+
+import base64
+import io
+import logging
+import os
+import tempfile
+from typing import Optional
+
+import torch
+import torchaudio
+from audiocraft.models import MusicGen
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# FastAPI app
+app = FastAPI(title="MusicGen API Server", version="1.0.0")
+
+# Global model instance
+model: Optional[MusicGen] = None
+model_name: str = os.getenv("MODEL_NAME", "facebook/musicgen-medium")
+device: str = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class AudioGenerationRequest(BaseModel):
+    """Music generation request"""
+    model: str = Field(default="musicgen-medium", description="Model name")
+    prompt: str = Field(..., description="Text description of the music to generate")
+    duration: float = Field(default=30.0, ge=1.0, le=30.0, description="Duration in seconds")
+    temperature: float = Field(default=1.0, ge=0.1, le=2.0, description="Sampling temperature")
+    top_k: int = Field(default=250, ge=0, le=500, description="Top-k sampling")
+    top_p: float = Field(default=0.0, ge=0.0, le=1.0, description="Top-p (nucleus) sampling")
+    cfg_coef: float = Field(default=3.0, ge=1.0, le=15.0, description="Classifier-free guidance coefficient")
+    response_format: str = Field(default="wav", description="Audio format (wav or mp3)")
+
+
+class AudioGenerationResponse(BaseModel):
+    """Music generation response"""
+    audio: str = Field(..., description="Base64-encoded audio data")
+    format: str = Field(..., description="Audio format (wav or mp3)")
+    duration: float = Field(..., description="Duration in seconds")
+    sample_rate: int = Field(..., description="Sample rate in Hz")
+
+
+@app.on_event("startup")
+async def startup_event():
+    """Load MusicGen model on startup"""
+    global model
+
+    logger.info(f"Loading MusicGen model: {model_name}")
+    logger.info(f"Device: {device}")
+
+    # Load model
+    model = MusicGen.get_pretrained(model_name, device=device)
+
+    logger.info(f"MusicGen model loaded successfully")
+    logger.info(f"Max duration: 30 seconds at 32kHz")
+
+
+@app.get("/")
+async def root():
+    """Root endpoint"""
+    return {
+        "service": "MusicGen API Server",
+        "model": model_name,
+        "device": device,
+        "max_duration": 30.0,
+        "sample_rate": 32000
+    }
+
+
+@app.get("/health")
+async def health():
+    """Health check endpoint"""
+    return {
+        "status": "healthy" if model else "initializing",
+        "model": model_name,
+        "device": device,
+        "ready": model is not None,
+        "gpu_available": torch.cuda.is_available()
+    }
+
+
+@app.post("/v1/audio/generations")
+async def generate_audio(request: AudioGenerationRequest) -> AudioGenerationResponse:
+    """Generate music from text prompt"""
+    if not model:
+        raise HTTPException(status_code=503, detail="Model not initialized")
+
+    logger.info(f"Generating music: {request.prompt[:100]}...")
+    logger.info(f"Duration: {request.duration}s, Temperature: {request.temperature}")
+
+    try:
+        # Set generation parameters
+        model.set_generation_params(
+            duration=request.duration,
+            temperature=request.temperature,
+            top_k=request.top_k,
+            top_p=request.top_p,
+            cfg_coef=request.cfg_coef,
+        )
+
+        # Generate audio
+        descriptions = [request.prompt]
+        with torch.no_grad():
+            wav = model.generate(descriptions)
+
+        # wav shape: [batch_size, channels, samples]
+        # Extract first batch item
+        audio_data = wav[0].cpu()  # [channels, samples]
+
+        # Get sample rate
+        sample_rate = model.sample_rate
+
+        # Save to temporary file
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
+            temp_path = temp_file.name
+            torchaudio.save(temp_path, audio_data, sample_rate)
+
+        # Read audio file and encode to base64
+        with open(temp_path, 'rb') as f:
+            audio_bytes = f.read()
+
+        # Clean up temporary file
+        os.unlink(temp_path)
+
+        # Encode to base64
+        audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
+
+        logger.info(f"Generated {request.duration}s of audio")
+
+        return AudioGenerationResponse(
+            audio=audio_base64,
+            format="wav",
+            duration=request.duration,
+            sample_rate=sample_rate
+        )
+
+    except Exception as e:
+        logger.error(f"Error generating audio: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/v1/models")
+async def list_models():
+    """List available models (OpenAI-compatible)"""
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": "musicgen-medium",
+                "object": "model",
+                "created": 1234567890,
+                "owned_by": "meta",
+                "permission": [],
+                "root": model_name,
+                "parent": None,
+            }
+        ]
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    host = os.getenv("HOST", "0.0.0.0")
+    port = int(os.getenv("PORT", "8000"))
+
+    logger.info(f"Starting MusicGen API server on {host}:{port}")
+
+    uvicorn.run(
+        app,
+        host=host,
+        port=port,
+        log_level="info",
+        access_log=True,
+    )