b68674fe64
Enhance MLFlowRepository and Activities classes with new methods and metrics - Added `check_artifact_exists` method to MLFlowRepository for verifying artifact presence in the MLflow Model Registry. - Implemented `get_prediction_data` method in MLFlowRepository to retrieve prediction data from models. - Updated Activities class to integrate ModelMetrics for improved metrics handling. - Enhanced tests for artifact existence checks and prediction data retrieval, ensuring robust coverage for new functionalities. - Updated various workflows to include `transform_table_name` in input data for better data handling.
134 lines
5.0 KiB
Python
134 lines
5.0 KiB
Python
from sientia_do.observability.metrics_controller import MetricsController
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from temporalio import workflow
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with workflow.unsafe.imports_passed_through():
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from typing import Any
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.observability.logger import Logger
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from laborious.activities.gates import Gates
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from laborious.activities.mlflow import MLFlow
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from laborious.activities.opc import OPC
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from laborious.activities.storage import Storage
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from laborious.activities.model_metrics import ModelMetrics
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class Activities(Storage, MLFlow, Gates, OPC, ModelMetrics):
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"""
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Main activities orchestrator for the Laborious system.
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This class combines functionality from multiple activity classes to provide
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a unified interface for all workflow operations. It manages database connections,
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MLFlow model interactions, data quality validation, and OPC server communications.
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The class implements multiple inheritance to combine specialized functionality:
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- Postgres: Database operations and data persistence
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- MLFlow: Model inference and transformation operations
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- Gates: Data quality validation and filtering mechanisms
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- OPC: Real-time data export to OPC servers
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Attributes:
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postgres_config (dict): PostgreSQL connection configuration
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mlflow_config (dict): MLFlow server configuration
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opc_config (dict): OPC server configuration
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logger (Logger): Logging and observability instance
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notification_handler (NotificationHandler): Notification management instance
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"""
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def __init__(
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self,
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postgres_config: dict[str, Any],
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mlflow_config: dict[str, Any],
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minio_config: dict[str, Any],
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opc_config: dict[str, Any],
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logger: Logger,
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notification_handler: NotificationHandler,
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):
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"""
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Initialize the Activities orchestrator with all required configurations.
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This constructor initializes all parent classes with their respective
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configurations and sets up the foundation for all activity operations.
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Args:
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postgres_config: PostgreSQL connection configuration dictionary
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Required keys: host, port, user, password, dbname, min_connections, max_connections
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mlflow_config: MLFlow server configuration dictionary
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Required keys: host, port, username, password
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opc_config: OPC server configuration dictionary
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Can contain multiple server configurations
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logger: Logger instance for observability and debugging
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notification_handler: Notification handler for alerts and monitoring
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Raises:
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Exception: If any parent class initialization fails
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"""
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metrics_controller = MetricsController(logger=logger)
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# Initialize parent classes
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Storage.__init__(
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self,
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host=postgres_config['host'],
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port=postgres_config['port'],
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user=postgres_config['user'],
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password=postgres_config['password'],
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dbname=postgres_config['dbname'],
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min_connections=postgres_config['min_connections'],
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max_connections=postgres_config['max_connections'],
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minio_config=minio_config,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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MLFlow.__init__(
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self,
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mlflow_host=mlflow_config['host'],
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mlflow_port=mlflow_config['port'],
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mlflow_username=mlflow_config['username'],
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mlflow_password=mlflow_config['password'],
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minio_config=minio_config,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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Gates.__init__(
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self,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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OPC.__init__(
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self,
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opc_servers=opc_config,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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ModelMetrics.__init__(
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self,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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async def shutdown(self):
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"""
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Gracefully shutdown all activities and clean up resources.
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This method ensures proper cleanup of all resources including:
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- PostgreSQL connection pools
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- OPC server connections
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- Any other resources that need explicit cleanup
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The method should be called before the application terminates to ensure
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proper resource cleanup and prevent resource leaks.
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"""
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Storage.close(self)
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MLFlow.close(self)
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Gates.close(self)
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await OPC.close(self)
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ModelMetrics.close(self) |