Profile and Optimize Factory LLM Throughput with vLLM and CTranslate2

"""\nProduction implementation for optimizing LLM throughput with vLLM and CTranslate2.\nProvides secure, scalable operations while ensuring data integrity.\n"""\nfrom typing import Dict, Any, List, Tuple\nimport os\nimport logging\nimport asyncio\nimport httpx\nimport backoff\n\n# Setup basic logging configuration\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nclass Config:\n    database_url: str = os.getenv('DATABASE_URL')  # Database URL from environment\n    api_key: str = os.getenv('API_KEY')  # API Key for authentication\n\nasync def validate_input(data: Dict[str, Any]) -> bool:\n    """Validate input data for processing.\n    \n    Args:\n        data: Input data to validate\n    Returns:\n        True if valid\n    Raises:\n        ValueError: If validation fails\n    """\n    if 'model' not in data or 'input' not in data:\n        raise ValueError('Missing required fields: model and input')\n    return True\n\ndef sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:\n    """Sanitize input fields.\n    \n    Args:\n        data: Raw input data\n    Returns:\n        Cleaned input data\n    """\n    return {k: v.strip() for k, v in data.items()}\n\nasync def fetch_data(url: str) -> Dict[str, Any]:\n    """Fetch data from a given URL using HTTP GET.\n    \n    Args:\n        url: URL to fetch data from\n    Returns:\n        JSON response as a dictionary\n    Raises:\n        HTTPError: If HTTP request fails\n    """\n    async with httpx.AsyncClient() as client:\n        response = await client.get(url)\n        response.raise_for_status()  # Raise an error for bad responses\n        return response.json()\n\nasync def save_to_db(data: Dict[str, Any]) -> None:\n    """Save processed data to the database.\n    \n    Args:\n        data: Data to save\n    Raises:\n        Exception: If saving fails\n    """\n    # Simulated db save operation\n    logger.info('Saving data to database...')\n    await asyncio.sleep(1)  # Simulate async DB operation\n    logger.info('Data saved successfully.')\n\n@backoff.on_exception(backoff.expo, Exception, max_tries=5)\nasync def call_api(model: str, input_data: str) -> Dict[str, Any]:\n    """Call the model API to get predictions.\n    \n    Args:\n        model: Model name to use\n        input_data: Data to send to the model\n    Returns:\n        API response as a dictionary\n    Raises:\n        Exception: If API call fails\n    """\n    url = f'https://api.example.com/models/{model}/predict'\n    logger.info(f'Calling API for model: {model}')\n    async with httpx.AsyncClient() as client:\n        response = await client.post(url, json={'input': input_data}, headers={'Authorization': f'Bearer {Config.api_key}'})\n        response.raise_for_status()\n        return response.json()\n\nasync def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n    """Process a batch of input data.\n    \n    Args:\n        data: List of input data dictionaries\n    Returns:\n        Processed data as a list of dictionaries\n    """\n    processed_results = []\n    for item in data:\n        try:\n            await validate_input(item)  # Validate input\n            sanitized_data = sanitize_fields(item)  # Sanitize input fields\n            result = await call_api(sanitized_data['model'], sanitized_data['input'])  # Call API\n            processed_results.append(result)  # Append result\n        except Exception as e:\n            logger.error(f'Error processing item {item}: {e}')\n            processed_results.append({'error': str(e)})  # Append error information\n    return processed_results\n\nclass LLMOptimizer:\n    """Main orchestrator for LLM optimization processes.\n    """\n    async def run(self, data: List[Dict[str, Any]]) -> None:\n        """Run the optimization process.\n        \n        Args:\n            data: List of input data dictionaries\n        """\n        logger.info('Starting LLM optimization process...')\n        results = await process_batch(data)  # Process input batch\n        await save_to_db(results)  # Save results to DB\n        logger.info('LLM optimization process completed.')\n\nif __name__ == '__main__':\n    data_to_process = [\n        {'model': 'gpt-3', 'input': 'Hello world'},\n        {'model': 'gpt-3', 'input': 'How are you?'}\n    ]\n    optimizer = LLMOptimizer()\n    asyncio.run(optimizer.run(data_to_process))\n    # Example usage of the LLM optimizer\n