Scale Distributed AI Training Across Clusters with Ray and ArgoCD

                      
                     
"""
Production implementation for scaling distributed AI training across clusters with Ray and ArgoCD.
Provides secure, reliable, and efficient operations.
"""

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

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

class Config:
    """Configuration class to manage environment variables."""
    ray_address: str = os.getenv('RAY_ADDRESS', 'auto')
    argo_cd_url: str = os.getenv('ARGO_CD_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 1.0))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for training jobs.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not isinstance(data, dict):
        raise ValueError('Input data must be a dictionary')
    if 'model' not in data or 'dataset' not in data:
        raise ValueError('Missing required fields: model, dataset')
    return True

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

@ray.remote
def train_model(model: str, dataset: str) -> Dict[str, Any]:
    """Train a model on the dataset.
    
    Args:
        model: Model name
        dataset: Dataset name
    Returns:
        Training metrics
    """
    logger.info(f'Starting training for model: {model} on dataset: {dataset}')
    # Simulate training logic
    sleep(2)  # Simulate a training delay
    return {'model': model, 'accuracy': 0.95}

def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch training data from an API.
    
    Args:
        api_url: API URL to fetch data from
    Returns:
        Data fetched from API
    Raises:
        Exception: If fetching fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error on bad response
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('Failed to fetch data')

def save_to_db(metrics: Dict[str, Any]) -> None:
    """Save training metrics to a database.
    
    Args:
        metrics: Metrics to save
    Raises:
        Exception: If saving fails
    """
    # Simulate saving logic
    logger.info(f'Saving metrics: {metrics}')

def call_api(url: str, payload: Dict[str, Any]) -> None:
    """Call an external API with the provided payload.
    
    Args:
        url: API URL
        payload: Data to send
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
    except requests.RequestException as e:
        logger.error(f'API call failed: {e}')
        raise Exception('Failed to call API')

def process_batch(data_batch: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Process a batch of training jobs.
    
    Args:
        data_batch: List of training job data
    Returns:
        Aggregated metrics
    """
    results = []
    for data in data_batch:
        sanitized_data = sanitize_fields(data)
        if validate_input(sanitized_data):
            result = ray.get(train_model.remote(sanitized_data['model'], sanitized_data['dataset']))
            results.append(result)
    return aggregate_metrics(results)

def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate training metrics from multiple results.
    
    Args:
        results: List of metric results
    Returns:
        Aggregated metrics
    """
    total_accuracy = sum(result['accuracy'] for result in results)
    return {'average_accuracy': total_accuracy / len(results)}

def retry_with_backoff(func: callable, *args, **kwargs) -> Any:
    """Retry a function call with exponential backoff.
    
    Args:
        func: Function to call
        *args: Positional arguments for the function
        **kwargs: Keyword arguments for the function
    Returns:
        Result of the function call
    """
    attempts = 0
    while attempts < Config.retry_attempts:
        try:
            return func(*args, **kwargs)
        except Exception as e:
            attempts += 1
            wait_time = Config.retry_delay * (2 ** attempts)
            logger.warning(f'Retry {attempts}/{Config.retry_attempts} after error: {e}')
            sleep(wait_time)
    raise Exception('All retries failed')  # Raise after exhausting attempts

# Main orchestrator class to tie everything together
class TrainingOrchestrator:
    """Orchestrator for distributed AI training jobs."""
    def __init__(self, api_url: str):
        self.api_url = api_url

    def run_training(self, job_data: Dict[str, Any]) -> None:
        """Run the training workflow.
        
        Args:
            job_data: Job data for training
        """
        try:
            # Fetch data from API
            data = retry_with_backoff(fetch_data, self.api_url)
            # Process training jobs
            metrics = process_batch(data['jobs'])
            # Save metrics to DB
            save_to_db(metrics)
            logger.info('Training completed successfully.')
        except Exception as e:
            logger.error(f'Error in training workflow: {e}')

if __name__ == '__main__':
    # Initialize Ray
    ray.init(address=Config.ray_address)
    orchestrator = TrainingOrchestrator(Config.argo_cd_url)
    # Example training job data
    example_job_data = {'jobs': [{'model': 'ResNet', 'dataset': 'CIFAR-10'}]}
    orchestrator.run_training(example_job_data)