SIENTIAPDE-1171

Update dependencies and enhance ML model retraining functionality

- Updated sientia-dataops-library version in requirements.txt from 1.3.3 to 1.3.4.
- Incremented image tag in values.yaml from 0.2.4 to 0.2.5 and added a new environment variable MONGODB_TTL_INDEX_HOURS.
- Introduced new methods in MLFlowRepository for model retraining and production model updates, including error handling and logging.
- Added retrain_model and update_production_model activities in mlflow.py to support model management workflows.
- Modified MongoDB connection settings in connectors_config.py for improved security and configuration flexibility.

laborious/worker/worker.py

@@ -6,6 +6,7 @@ with workflow.unsafe.imports_passed_through():
     import os
     import sys
     import asyncio
+    from laborious.workflows.minimal_retrain import MinimalRetrain
     from laborious.workflows.predictions_batch import PredictionsBatch
     from laborious.workflows.sub_workflows.prediction_process import PredictionProcess
     from laborious.workflows.sub_workflows.format_and_export_prediction import \
@@ -51,12 +52,28 @@ async def main():
 
     temporal_client = await client.Client.connect(
         target_host=host,
-        namespace=os.getenv('TEMPORAL_NAMESPACE', 'default')
+        namespace=os.getenv('TEMPORAL_NAMESPACE', 'laborious')
     )
 
     logger.info('Starting Workers...')
 
     workers = [
+        Worker(
+            temporal_client,
+            task_queue='minimal_retrain-queue',
+            workflows=[MinimalRetrain],
+            activities=[
+                activities.load_custom_query,
+                activities.retrain_model,
+                activities.update_production_model,
+                activities.export_data_to_postgres
+            ],
+            max_concurrent_workflow_tasks=100,
+            max_concurrent_activities=100,
+            max_concurrent_local_activities=100,
+            max_concurrent_workflow_task_polls=100,
+            max_cached_workflows=50,
+        ),
         Worker(
             temporal_client,
             task_queue='predictions_batch-queue',