Run Multi-Model Inference Pipelines on Factory Edge with ExecuTorch and ONNX Runtime

                      
                     
"""
Production implementation for running multi-model inference pipelines on the factory edge using ExecuTorch and ONNX Runtime.
Provides secure, scalable operations for real-time data processing and inference.
"""
from typing import Dict, Any, List
import os
import logging
import onnxruntime
import torch
import time

# Setup logging for tracking execution flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment settings."""
    model_paths: List[str] = os.getenv('MODEL_PATHS').split(',')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for inference.
    
    Args:
        data: Input data dictionary containing features for models.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not isinstance(data, dict):
        raise ValueError('Input data must be a dictionary.')
    if 'features' not in data:
        raise ValueError('Missing features in the input data.')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data dictionary.
    Returns:
        Dict[str, Any]: Sanitized input data.
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data

def load_model(model_path: str):
    """Load an ONNX model from the specified path.
    
    Args:
        model_path: Path to the model file.
    Returns:
        onnxruntime.InferenceSession: Loaded ONNX model session.
    """
    return onnxruntime.InferenceSession(model_path)

def normalize_data(data: Dict[str, Any]) -> List[float]:
    """Normalize input features for model inference.
    
    Args:
        data: Input data containing features.
    Returns:
        List[float]: Normalized feature values.
    """
    # Example normalization
    return [float(value) / 100.0 for value in data['features']]

def process_batch(models: List[onnxruntime.InferenceSession], input_data: List[float]) -> List[Dict[str, Any]]:
    """Process a batch of data through multiple models.
    
    Args:
        models: List of loaded ONNX models.
        input_data: List of normalized feature values.
    Returns:
        List[Dict[str, Any]]: Results from each model.
    """
    results = []
    for model in models:
        result = model.run(None, {'input': input_data})[0]
        results.append(result)
    return results

def fetch_data(source: str) -> Dict[str, Any]:
    """Fetch data from a specified source.
    
    Args:
        source: Data source identifier.
    Returns:
        Dict[str, Any]: Fetched data.
    """
    # Placeholder for actual data fetching logic
    return {'features': [10, 20, 30]}

def retry(func):
    """Decorator for retrying function execution on failure.
    
    Args:
        func: Function to be retried.
    """
    def wrapper(*args, **kwargs):
        attempts = 0
        while attempts < Config.retry_attempts:
            try:
                return func(*args, **kwargs)
            except Exception as e:
                logger.warning(f'Retry attempt {attempts + 1} failed: {e}')
                attempts += 1
                time.sleep(2 ** attempts)  # Exponential backoff
        raise Exception('Function failed after multiple attempts.')
    return wrapper

class InferencePipeline:
    """Class to orchestrate inference pipeline logic."""
    def __init__(self):
        self.models = [load_model(path) for path in Config.model_paths]

    @retry
    def run(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Run the inference pipeline on the provided data.
        
        Args:
            data: Input data for inference.
        Returns:
            List[Dict[str, Any]]: Inference results from all models.
        """
        validate_input(data)
        sanitized_data = sanitize_fields(data)
        normalized_data = normalize_data(sanitized_data)
        results = process_batch(self.models, normalized_data)
        return results

if __name__ == '__main__':
    # Example usage of the inference pipeline
    pipeline = InferencePipeline()
    input_data = fetch_data('data_source_1')
    try:
        results = pipeline.run(input_data)
        logger.info(f'Inference results: {results}')
    except Exception as e:
        logger.error(f'Error during inference: {e}')