Deploy Inference Pipelines with Triton Inference Server and NVIDIA Model-Optimizer

                      
                     
"""
Production implementation for deploying inference pipelines.
Provides secure, scalable operations with Triton Inference Server and NVIDIA Model-Optimizer.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, validator
from time import sleep

# Logger setup for tracking application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration for environment variables.
    """
    model_repository: str = os.getenv('MODEL_REPOSITORY', '/models')
    triton_url: str = os.getenv('TRITON_URL', 'http://localhost:8000')

class InferenceRequest(BaseModel):
    """
    Request model for inference.
    """
    input_data: List[float]

    @validator('input_data')
    def validate_input_data(cls, v):
        """
        Validate input data for inference.
        
        Args:
            cls: Class reference
            v: List of float values
        Returns:
            Validated input data
        Raises:
            ValueError: If validation fails
        """
        if len(v) == 0:
            raise ValueError('Input data cannot be empty')
        return v

async def fetch_model_metadata(model_name: str) -> Dict[str, Any]:
    """
    Fetch model metadata from Triton Inference Server.
    
    Args:
        model_name: Name of the model
    Returns:
        Metadata of the model
    Raises:
        HTTPException: If request fails
    """
    response = requests.get(f'{Config.triton_url}/v2/models/{model_name}')
    if response.status_code != 200:
        raise HTTPException(status_code=response.status_code, detail=response.text)
    return response.json()

async def call_inference(model_name: str, input_data: List[float]) -> Dict[str, Any]:
    """
    Call Triton Inference Server for prediction.
    
    Args:
        model_name: Name of the model
        input_data: Input data for prediction
    Returns:
        Response from the inference server
    Raises:
        HTTPException: If inference call fails
    """
    payload = {
        "inputs": [{
            "name": "input_tensor",
            "shape": [1, len(input_data)],
            "datatype": "FP32",
            "data": input_data,
        }]
    }
    response = requests.post(f'{Config.triton_url}/v2/models/{model_name}/infer', json=payload)
    if response.status_code != 200:
        raise HTTPException(status_code=response.status_code, detail=response.text)
    return response.json()

async def process_inference_request(model_name: str, input_data: List[float]) -> Dict[str, Any]:
    """
    Process the inference request and call the model.
    
    Args:
        model_name: Name of the model to call
        input_data: Input data for inference
    Returns:
        Result from the inference call
    Raises:
        HTTPException: If processing fails
    """
    try:
        metadata = await fetch_model_metadata(model_name)  # Fetch model metadata
        logger.info(f'Metadata for model {model_name}: {metadata}')  # Log metadata
        result = await call_inference(model_name, input_data)  # Call model inference
        logger.info(f'Inference result: {result}')  # Log result
        return result
    except HTTPException as e:
        logger.error(f'Error processing inference: {e.detail}')  # Log error details
        raise
    except Exception as e:
        logger.error(f'Unexpected error: {str(e)}')  # Log any unexpected errors
        raise HTTPException(status_code=500, detail='Internal server error')

# FastAPI application setup
app = FastAPI()

@app.post('/predict/{model_name}')
async def predict(model_name: str, request: InferenceRequest) -> Dict[str, Any]:
    """
    Endpoint to handle inference requests.
    
    Args:
        model_name: Name of the model to predict
        request: Inference request data
    Returns:
        Inference results
    Raises:
        HTTPException: If model prediction fails
    """
    logger.info(f'Received prediction request for model: {model_name}')  # Log incoming request
    result = await process_inference_request(model_name, request.input_data)  # Process inference
    return result  # Return the result

if __name__ == '__main__':
    # Example usage
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)  # Start the FastAPI server