SIENTIAPDE-1717: Remove MinIO cleanup functionality and associated components. This change streamlines the cleanup workflow to focus solely on local temporary directories, removes the ModelTrainingError exception, and updates related configurations, documentation, and tests.

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,