SIENTIAPDE-1231

Enhance MLFlowRepository memory management by adding garbage collection and logging for model deletion

- Introduced garbage collection after model deletion to optimize memory usage.
- Added logging to inform when a model is deleted from memory, improving traceability during predictions.

laborious/utils/repository/model_repository.py

@@ -27,7 +27,7 @@ import gzip
 import pickle
 from numpy import ndarray
 from typing import Any
-
+import gc
 from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
 
 ARTIFACTS_PATH = "./tmp/artifacts"
@@ -613,8 +613,12 @@ class MLFlowRepository():
         prediction = model.predict(data)
 
         if retention == 0:
+            self.logger.info(
+                f"Deleting model {model_name} from memory")
             del model
 
+        gc.collect()
+
         return prediction
 
     def get_cached_predict(self, model_name: str, data: pd.DataFrame, retention: int,
@@ -638,8 +642,12 @@ class MLFlowRepository():
         prediction = model.predict(data)
 
         if retention == 0:
+            self.logger.info(
+                f"Deleting model {model_name} from memory")
             del model
 
+        gc.collect()
+
         return prediction
 
     """