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.
115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
from temporalio import workflow
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with workflow.unsafe.imports_passed_through():
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from datetime import timedelta
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from typing import Any
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from sientia_do.temporal.policies import retry_policy
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from model_manager.activities.activities import Activities
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@workflow.defn(name='minimal_retrain')
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class MinimalRetrain:
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"""
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Automated model retraining workflow for the Model Manager system.
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This workflow implements a complete model retraining pipeline that loads
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training data, executes model retraining, updates production models,
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and maintains comprehensive audit trails. It's designed for automated
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model lifecycle management with minimal manual intervention.
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The workflow provides a robust retraining process with:
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- Automated data loading from configured data sources
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- MLFlow model retraining with quality validation
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- Production model updates with version control
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- Comprehensive reporting and audit trail maintenance
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- Error handling and notification integration
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"""
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@workflow.run
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async def run(self, input_data: dict[str, Any]):
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"""
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Execute the automated model retraining workflow.
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This method orchestrates the complete model retraining process by:
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1. Loading training data using the provided custom SQL query
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2. Executing MLFlow model retraining with the loaded data
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3. Updating production models with newly trained versions
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4. Persisting comprehensive retraining reports to database
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The method implements comprehensive error handling and ensures all
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required parameters are properly configured before proceeding.
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Args:
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input_data: Complete configuration for the retraining workflow
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Required keys:
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- schedule_name (str): Schedule identifier for the retraining
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- model_name (str): Name of the ML model to retrain
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- model_id (int): Unique identifier for the model version
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- query (str): SQL query for training data loading
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- schema (str, optional): Database schema for report storage
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- table_name (str, optional): Target table for retraining reports
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- datetime_columns (list[str], optional): Columns to treat as datetime
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Returns:
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None: The workflow completes successfully when all steps finish
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Raises:
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Exception: If any required parameters are missing or if the workflow fails
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during data loading, retraining, or model update operations
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"""
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metadata = {
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'metadata': {
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'schedule_name': input_data['schedule_name'],
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'model_name': input_data['model_name'],
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'model_id': input_data['model_id'],
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'workflow_name': 'minimal_retrain',
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}
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}
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model_name = input_data['model_name']
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data = await workflow.execute_local_activity_method(
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Activities.load_custom_query,
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{
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**metadata,
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'query': input_data['query'],
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'datetime_columns': input_data.get('datetime_columns', []),
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=60),
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)
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experiment_response = await workflow.execute_activity_method(
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Activities.retrain_model,
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{**metadata, 'data': data, 'model_name': model_name},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=60),
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)
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report = await workflow.execute_activity_method(
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Activities.update_production_model,
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{
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**metadata,
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'model_name': model_name,
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'model_id': input_data['model_id'],
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**experiment_response,
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=60),
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)
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await workflow.execute_activity_method(
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Activities.export_data_to_postgres,
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{
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**metadata,
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'data': report,
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'schema': input_data['schema'],
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'table_name': input_data['table_name'],
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(seconds=60),
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)
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