Merge branch 'main' into release/SIENTIAPDE-1645

model_manager/workflows/cleanup_files.py

@@ -1,5 +1,5 @@
 """
-Cleanup workflow for removing stale files from MinIO and local filesystem.
+Cleanup workflow for removing local filesystem.
 
 This module provides a Temporal cron workflow that runs daily to clean up
 temporary files and directories older than the configured retention period.
@@ -13,11 +13,9 @@ with workflow.unsafe.imports_passed_through():
     from typing import Any
 
     from model_manager.activities.activities import Activities
-    from model_manager.workflows.train_model import POD_ID, network_retry_policy, no_retry_policy
+    from model_manager.workflows.train_model import POD_ID, no_retry_policy
 
-    TIMEOUT_CLEANUP_MINIO = int(os.getenv('TIMEOUT_CLEANUP_MINIO', '300'))
     TIMEOUT_CLEANUP_LOCAL = int(os.getenv('TIMEOUT_CLEANUP_LOCAL', '120'))
-    DEFAULT_CLEANUP_BUCKET = os.getenv('DEFAULT_CLEANUP_BUCKET', 'model-training')
 
 
 @workflow.defn(name='cleanup_files')
@@ -26,7 +24,6 @@ class CleanupFiles:
     Cleanup workflow for removing stale files.
 
     This workflow cleans up:
-    - MinIO files with timestamp prefixes
     - Local temporary directories with timestamp suffixes
 
     The workflow is designed to be simple and robust, with error handling
@@ -38,17 +35,10 @@ class CleanupFiles:
         """
         Execute the cleanup workflow.
 
-        This method orchestrates the cleanup of MinIO files and local directories
+        This method orchestrates the cleanup of local directories
         in sequence. No exception handling is needed as activities handle their
         own errors and notifications.
-
-        Args:
-            input_data: Workflow configuration containing optional:
-                - bucket_name (str): Bucket to clean (defaults to environment variable)
         """
-        # Get bucket name from input or environment
-        bucket_name = input_data.get('bucket_name', DEFAULT_CLEANUP_BUCKET)
-
         # Default temp path for local cleanup
         temp_path = 'model_manager/reports/temp'
 
@@ -60,17 +50,6 @@ class CleanupFiles:
             }
         }
 
-        # Execute MinIO cleanup
-        await workflow.execute_activity_method(
-            Activities.cleanup_minio_files,
-            {
-                **metadata,
-                'bucket_name': bucket_name,
-            },
-            retry_policy=network_retry_policy,
-            start_to_close_timeout=timedelta(seconds=TIMEOUT_CLEANUP_MINIO),
-        )
-
         # Execute local directory cleanup
         await workflow.execute_activity_method(
             Activities.cleanup_temp_directories,

model_manager/workflows/train_model.py

@@ -20,7 +20,6 @@ with workflow.unsafe.imports_passed_through():
 
     from model_manager.activities.activities import Activities
     from model_manager.activities.experiment_tracking import UpdateType
-    from model_manager.utils.exceptions import ModelTrainingError
     from model_manager.utils.models.experiment_status import ExperimentStatus
     from model_manager.utils.models.train_model_params import TrainModelParams
 
@@ -273,7 +272,7 @@ class TrainModel:
                 metadata=metadata,
                 experiment_run_id=experiment_run_id,
                 update_type=UpdateType.MODEL_SAVED,
-                status=ExperimentStatus.TRACKING_SENT,
+                status=ExperimentStatus.TRAINING_SUCCESS,
                 run_name=train_result.get('run_name'),
             )
 
@@ -310,7 +309,7 @@ class TrainModel:
         metadata: dict[str, Any],
     ) -> None:
         """
-        Cleanup resources and delete file from MinIO.
+        Cleanup resources.
 
         This method deletes the training file from MinIO. On success, updates
         DB status to FILE_DELETED.
@@ -319,9 +318,6 @@ class TrainModel:
         Args:
             experiment_run_id: Validated experiment run ID
             metadata: Workflow execution metadata
-
-        Raises:
-            Exception: If cleanup fails (after updating DB status)
         """
         try:
             await workflow.execute_activity_method(