Deploy Model Inference with Triton Server and ArgoCD

                      
                     
"""
Production implementation for Deploying Model Inference with Triton Server and ArgoCD.
Provides secure, scalable operations for serving ML models.
"""

from typing import Dict, Any, List
import os
import logging
import requests
import time

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    triton_url: str = os.getenv('TRITON_URL', 'http://localhost:8000')
    model_name: str = os.getenv('MODEL_NAME', 'my_model')
    max_retries: int = 5
    retry_delay: int = 2  # seconds

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'input_data' not in data:
        raise ValueError('Missing input_data')  # Check for required field
    return True  # Validation passed

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for security.
    
    Args:
        data: Input data to sanitize
    Returns:
        Cleaned data
    """
    # Simple sanitation, could be extended for security
    return {k: str(v).strip() for k, v in data.items()}

async def normalize_data(data: Dict[str, Any]) -> List[float]:
    """Normalize input data for model inference.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data as a list
    """
    # Example normalization, adjust as necessary
    return [float(x) / 100 for x in data['input_data']]

async def call_triton_model(normalized_data: List[float]) -> Dict[str, Any]:
    """Call Triton model for inference.
    
    Args:
        normalized_data: Data to send to the model
    Returns:
        Inference response
    Raises:
        Exception: If request to Triton fails
    """
    url = f"{Config.triton_url}/v2/models/{Config.model_name}/infer"
    payload = {"inputs": [{"name": "input_0", "data": normalized_data}]}
    for attempt in range(Config.max_retries):
        try:
            response = requests.post(url, json=payload)
            response.raise_for_status()  # Raise an exception for HTTP errors
            return response.json()  # Return the inference result
        except requests.RequestException as e:
            logger.error(f'Error calling Triton model: {e}')
            if attempt < Config.max_retries - 1:
                time.sleep(Config.retry_delay)  # Exponential backoff could be implemented
    raise Exception('Max retries exceeded in model inference')

async def process_batch(data: Dict[str, Any]) -> Dict[str, Any]:
    """Process a batch of input data.
    
    Args:
        data: Input data to process
    Returns:
        Processed inference results
    """
    await validate_input(data)  # Validate input
    sanitized_data = await sanitize_fields(data)  # Sanitize fields
    normalized_data = await normalize_data(sanitized_data)  # Normalize data
    inference_result = await call_triton_model(normalized_data)  # Call Triton
    return inference_result  # Return the result

async def format_output(inference_result: Dict[str, Any]) -> str:
    """Format the output for user presentation.
    
    Args:
        inference_result: Result from model inference
    Returns:
        Formatted string
    """
    # Simple formatting logic
    return f'Inference result: {inference_result}'

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: The function to wrap
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            return {'error': str(e)}  # Return error response
    return wrapper

class ModelInferenceOrchestrator:
    """Orchestrator to manage the inference workflow.
    """

    @handle_errors
    async def execute_inference(self, input_data: Dict[str, Any]) -> str:
        """Execute the full inference workflow.
        
        Args:
            input_data: Data to infer
        Returns:
            Formatted inference result
        """
        result = await process_batch(input_data)  # Process the batch
        formatted_result = await format_output(result)  # Format the response
        return formatted_result  # Return formatted result

if __name__ == '__main__':
    import asyncio
    sample_input = {'input_data': [10, 20, 30]}  # Sample input data
    orchestrator = ModelInferenceOrchestrator()  # Create orchestrator instance
    result = asyncio.run(orchestrator.execute_inference(sample_input))  # Run inference
    print(result)  # Output result