from temporalio import workflow with workflow.unsafe.imports_passed_through(): from typing import Any from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler from sientia_do.observability.logger import Logger from model_manager.activities.experiment_tracking import ExperimentTracking from model_manager.activities.gates import Gates from model_manager.activities.minio import MinIO from model_manager.activities.mlflow import MLFlow from model_manager.activities.training import Training class Activities(ExperimentTracking, MLFlow, MinIO, Gates, Training): """ Main activities orchestrator for the Model Manager system. This class combines functionality from multiple activity classes to provide a unified interface for all workflow operations. It manages database connections, MLFlow model interactions, MinIO storage operations, and data quality validation. The class implements multiple inheritance to combine specialized functionality: - ExperimentTracking: ML experiment lifecycle tracking and database operations (extends Postgres) - MLFlow: Model inference and transformation operations - MinIO: Object storage operations (file upload/download/delete) - Gates: Data quality validation and filtering mechanisms - Training: ML model training operations (extends BaseActivity) Attributes: postgres_config (dict): PostgreSQL connection configuration mlflow_config (dict): MLFlow server configuration minio_config (dict): MinIO storage configuration logger (Logger): Logging and observability instance notification_handler (NotificationHandler): Notification management instance """ def __init__( self, postgres_config: dict[str, Any], mlflow_config: dict[str, Any], minio_config: dict[str, Any], logger: Logger, notification_handler: NotificationHandler, ): """ Initialize the Activities orchestrator with all required configurations. This constructor initializes all parent classes with their respective configurations and sets up the foundation for all activity operations. Args: postgres_config: PostgreSQL connection configuration dictionary Required keys: host, port, user, password, dbname, min_connections, max_connections mlflow_config: MLFlow server configuration dictionary Required keys: host, port, username, password minio_config: MinIO storage configuration dictionary Required keys: endpoint_url, access_key, secret_key, region, use_ssl logger: Logger instance for observability and debugging notification_handler: Notification handler for alerts and monitoring Raises: Exception: If any parent class initialization fails """ # Initialize parent classes ExperimentTracking.__init__( self, host=postgres_config['host'], port=postgres_config['port'], user=postgres_config['user'], password=postgres_config['password'], dbname=postgres_config['dbname'], min_connections=postgres_config['min_connections'], max_connections=postgres_config['max_connections'], logger=logger, notification_handler=notification_handler, ) MLFlow.__init__( self, mlflow_host=mlflow_config['host'], mlflow_port=mlflow_config['port'], mlflow_username=mlflow_config['username'], mlflow_password=mlflow_config['password'], logger=logger, notification_handler=notification_handler, ) MinIO.__init__( self, endpoint_url=minio_config['endpoint_url'], access_key=minio_config['access_key'], secret_key=minio_config['secret_key'], region=minio_config['region'], use_ssl=minio_config['use_ssl'], max_retry_attempts=minio_config['max_retry_attempts'], retry_mode=minio_config['retry_mode'], connect_timeout=minio_config['connect_timeout'], read_timeout=minio_config['read_timeout'], logger=logger, notification_handler=notification_handler, ) Gates.__init__(self, logger=logger, notification_handler=notification_handler) Training.__init__(self, logger=logger, notification_handler=notification_handler) async def shutdown(self): """ Gracefully shutdown all activities and clean up resources. This method ensures proper cleanup of all resources including: - PostgreSQL connection pools (via ExperimentTracking) - Any other resources that need explicit cleanup The method should be called before the application terminates to ensure proper resource cleanup and prevent resource leaks. """ ExperimentTracking.close(self)