diff --git a/AIAgent/run_training.py b/AIAgent/run_training.py
index d0992e2..39173f0 100644
--- a/AIAgent/run_training.py
+++ b/AIAgent/run_training.py
@@ -129,35 +129,30 @@ def model_init(**model_params) -> nn.Module:
         epochs=training_config.epochs,
         val_config=validation_config,
     )
-    try:
-        if optuna_config.study_uri is None and weights_uri is None:
-
-            def save_study(study, _):
-                joblib.dump(
-                    study,
-                    CURRENT_STUDY_PATH,
-                )
-                with mlflow.start_run(mlflow.last_active_run().info.run_id):
-                    mlflow.log_artifact(CURRENT_STUDY_PATH)
-
-            study = optuna.create_study(
-                sampler=sampler, direction=optuna_config.study_direction
-            )
-            study.optimize(
-                objective_partial,
-                n_trials=optuna_config.n_trials,
-                gc_after_trial=True,
-                n_jobs=optuna_config.n_jobs,
-                callbacks=[save_study],
-            )
-        else:
-            downloaded_artifact_path = mlflow.artifacts.download_artifacts(
-                optuna_config.study_uri, dst_path=str(REPORT_PATH)
+    if optuna_config.study_uri is None and weights_uri is None:
+        def save_study(study, _):
+            joblib.dump(
+                study,
+                CURRENT_STUDY_PATH,
             )
-            study: optuna.Study = joblib.load(downloaded_artifact_path)
-            objective_partial(study.best_trial)
-    except RuntimeError:
-        logging.error("Fail to train")
+            with mlflow.start_run(mlflow.last_active_run().info.run_id):
+                mlflow.log_artifact(CURRENT_STUDY_PATH)
+        study = optuna.create_study(
+            sampler=sampler, direction=optuna_config.study_direction
+        )
+        study.optimize(
+            objective_partial,
+            n_trials=optuna_config.n_trials,
+            gc_after_trial=True,
+            n_jobs=optuna_config.n_jobs,
+            callbacks=[save_study],
+        )
+    else:
+        downloaded_artifact_path = mlflow.artifacts.download_artifacts(
+            optuna_config.study_uri, dst_path=str(REPORT_PATH)
+        )
+        study: optuna.Study = joblib.load(downloaded_artifact_path)
+        objective_partial(study.best_trial)
 
 
 def objective(