dfc190c818
This commit includes several changes: - Reorganized imports and class inheritance in activities.py, gates.py and mlflow.py for better readability and maintainability. - Improved error handling and logging in gates.py and mlflow.py. - Added input validation and filtering in gates.py to ensure data quality. - Enhanced prediction formatting and storage policy management in gates.py. - Updated metrics.py to use consistent naming conventions and labels. - Refactored connectors_config.py to use type hints and improve code clarity. - Updated conditional and MLFlow filters for better data quality checks. - Improved model repository logic for retraining and updating models. - Enhanced worker.py to include SDK metrics and improved error handling. - Refactored workflows for better modularity and error handling. - Updated tests to reflect the changes and improve test coverage.
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
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 sientia_do.temporal.activities.postgres import Postgres
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from model_manager.activities.gates import Gates
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from model_manager.activities.mlflow import MLFlow
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class Activities(Postgres, MLFlow, Gates):
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"""
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Main activities orchestrator for the Model Manager 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, and data quality validation.
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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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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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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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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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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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# Initialize parent classes
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Postgres.__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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logger=logger,
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notification_handler=notification_handler,
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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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logger=logger,
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notification_handler=notification_handler,
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)
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Gates.__init__(self, logger=logger, notification_handler=notification_handler)
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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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- 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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Postgres.close(self)
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