Store and Version Industrial AI Model Artifacts at Scale with MinIO and Ray

"""
Production implementation for storing and versioning industrial AI model artifacts at scale using MinIO and Ray.
Provides secure, scalable operations for managing AI models.
"""
from typing import Dict, Any, List
import os
import logging
import ray
import minio
from minio import Minio
from minio.error import S3Error

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    minio_url: str = os.getenv('MINIO_URL')
    minio_access_key: str = os.getenv('MINIO_ACCESS_KEY')
    minio_secret_key: str = os.getenv('MINIO_SECRET_KEY')
    bucket_name: str = os.getenv('BUCKET_NAME')

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 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'version' not in data:
        raise ValueError('Missing version')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    for key in data.keys():
        data[key] = str(data[key]).strip()  # Sanitize input fields
    return data

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Assuming model_id is an integer and version is a string
    data['model_id'] = int(data['model_id'])
    return data

def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform records for storage.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    # Example transformation
    data['metadata'] = {'version': data['version'], 'timestamp': os.time()}
    return data

def fetch_data(model_id: int) -> Dict[str, Any]:
    """Fetch model data from the storage.
    
    Args:
        model_id: ID of the model to fetch
    Returns:
        Model data
    Raises:
        S3Error: If fetching data fails
    """
    try:
        client = Minio(Config.minio_url,
                        access_key=Config.minio_access_key,
                        secret_key=Config.minio_secret_key)
        logger.info(f'Fetching data for model_id: {model_id}')
        # Fetching data logic goes here
        return {}  # Placeholder for fetched data
    except S3Error as e:
        logger.error(f'Error fetching data: {e}')
        raise

def save_to_db(data: Dict[str, Any]) -> None:
    """Save transformed data to MinIO storage.
    
    Args:
        data: Data to save
    Raises:
        S3Error: If saving data fails
    """
    try:
        client = Minio(Config.minio_url,
                        access_key=Config.minio_access_key,
                        secret_key=Config.minio_secret_key)
        logger.info(f'Saving data for model_id: {data.get('model_id')}')
        # Saving logic goes here
    except S3Error as e:
        logger.error(f'Error saving data: {e}')
        raise

def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of model artifacts.
    
    Args:
        batch: List of model artifacts to process
    """
    for record in batch:
        try:
            validate_input(record)  # Validate input
            sanitized_data = sanitize_fields(record)  # Sanitize input
            normalized_data = normalize_data(sanitized_data)  # Normalize data
            transformed_data = transform_records(normalized_data)  # Transform data
            save_to_db(transformed_data)  # Save to MinIO
        except Exception as e:
            logger.error(f'Error processing record {record}: {e}')  # Log error

class ModelArtifactManager:
    """Main orchestrator class to manage model artifacts.
    """
    def __init__(self) -> None:
        ray.init()  # Initialize Ray for distributed processing

    def run(self, model_data: List[Dict[str, Any]]) -> None:
        """Run the processing pipeline.
        
        Args:
            model_data: List of model data
        """
        logger.info('Starting model artifact processing')
        process_batch(model_data)  # Process the provided model data

if __name__ == '__main__':
    # Example usage of the ModelArtifactManager
    manager = ModelArtifactManager()
    example_data = [
        {'model_id': 1, 'version': 'v1.0'},
        {'model_id': 2, 'version': 'v2.0'}
    ]
    manager.run(example_data)