SIENTIAPDE-1350: Integrate cleanup workflow and update default task queue names.

model_manager/activities/activities.py

@@ -20,13 +20,12 @@ class Activities(ExperimentTracking, Training, Cleanup):
 
     This class combines functionality from multiple activity classes to provide
     a unified interface for all workflow operations. It manages database connections,
-    MLFlow model interactions, MinIO storage operations, and data quality validation.
+    MLFlow model interactions, MinIO storage operations, and cleanup operations.
 
     The class implements multiple inheritance to combine specialized functionality:
-    - ExperimentTracking: ML experiment lifecycle tracking and database operations (extends Postgres)
-    - MLFlow: Model saving and artifact management operations
-    - MinIO: Object storage operations (file upload/download/delete)
-    - Training: ML model training operations (extends BaseActivity)
+    - ExperimentTracking: ML experiment lifecycle tracking and database operations
+    - Training: ML model training operations with MLFlow and MinIO integration
+    - Cleanup: File and directory cleanup operations for MinIO and local filesystem
 
     Attributes:
         postgres_config (dict): PostgreSQL connection configuration

model_manager/schedules/cleanup_schedule.py

@@ -15,7 +15,7 @@ from temporalio.client import (
 SCHEDULE_ID = os.getenv('CLEANUP_SCHEDULE_ID', 'cleanup-files-daily')
 CLEANUP_CRON = os.getenv('CLEANUP_CRON', '0 0 * * *')  # Default: midnight UTC
 CLEANUP_TIMEZONE = os.getenv('CLEANUP_TIMEZONE', 'UTC')
-CLEANUP_TASK_QUEUE = os.getenv('CLEANUP_TASK_QUEUE', 'cleanup_queue')
+CLEANUP_TASK_QUEUE = os.getenv('CLEANUP_TASK_QUEUE', 'cleanup-queue')
 CLEANUP_EXECUTION_TIMEOUT_HOURS = int(os.getenv('CLEANUP_EXECUTION_TIMEOUT_HOURS', '1'))
 
 
@@ -28,6 +28,8 @@ async def schedule_exists(
     Args:
         client: Temporal client instance
         schedule_id: ID of the schedule to check
+        logger: Logger instance for error logging
+        metadata: Metadata dictionary for logging context
 
     Returns:
         True if schedule exists, False otherwise
@@ -53,6 +55,8 @@ async def create_cleanup_schedule(
 
     Args:
         client: Temporal client instance
+        logger: Logger instance for logging schedule operations
+        metadata: Metadata dictionary for logging context
     """
     # Check if schedule already exists
     if await schedule_exists(client, SCHEDULE_ID, logger, metadata):

model_manager/worker/worker.py

@@ -2,9 +2,11 @@
 
 This module provides the main worker implementation for the Sientia DataOps Model Manager system.
 It orchestrates Temporal workers, manages task queues, and handles the lifecycle of
-model training workflows.
+model training and cleanup workflows.
 
-The worker supports the train_model-queue task queue for ML model training workflows.
+The worker supports two task queues:
+- train_model-queue: For ML model training workflows
+- cleanup-queue: For file cleanup workflows
 
 Key Features:
 - Automatic scaling with PollerBehaviorAutoscaling
@@ -12,14 +14,18 @@ Key Features:
 - Comprehensive error handling and logging
 - Graceful shutdown with cleanup
 - ML model training pipeline orchestration
+- Automated cleanup schedule management
 
 Environment Variables:
 - TEMPORAL_HOST: Temporal server address (default: localhost:7233)
-- TEMPORAL_NAMESPACE: Temporal namespace (default: model_manager)
+- TEMPORAL_NAMESPACE: Temporal namespace (default: model-manager)
+- TEMPORAL_USE_TLS: Enable TLS for Temporal connection (default: false)
+- TRAIN_TASK_QUEUE: Task queue for training workflows (default: train_model-queue)
+- CLEANUP_TASK_QUEUE: Task queue for cleanup workflows (default: cleanup-queue)
 - POD_ID: Kubernetes pod identifier for metrics
 - HTTP_METRICS_PORT: Prometheus metrics server port (default: 9090)
 - HTTP_SDK_METRICS_PORT: Temporal SDK metrics port (default: 9091)
-- PROJECT_NAME: Project name for notifications (default: model_manager)
+- PROJECT_NAME: Project name for notifications (default: model-manager)
 """
 
 from temporalio import client, workflow

tests/schedules/test_cleanup_schedule.py

@@ -238,7 +238,7 @@ async def test_create_cleanup_schedule_uses_defaults(mock_temporal_client, mock_
 
     assert schedule_obj.spec.cron_expressions == ['0 0 * * *']  # Default midnight
     assert schedule_obj.spec.time_zone_name == 'UTC'  # Default UTC
-    assert schedule_obj.action.task_queue == 'cleanup_queue'  # Default queue
+    assert schedule_obj.action.task_queue == 'cleanup-queue'  # Default queue
     assert schedule_obj.action.execution_timeout == timedelta(hours=1)  # Default 1 hour
 
 
@@ -358,5 +358,5 @@ def test_environment_variables_use_defaults_when_not_set():
     assert SCHEDULE_ID == 'cleanup-files-daily'
     assert CLEANUP_CRON == '0 0 * * *'
     assert CLEANUP_TIMEZONE == 'UTC'
-    assert CLEANUP_TASK_QUEUE == 'cleanup_queue'
+    assert CLEANUP_TASK_QUEUE == 'cleanup-queue'
     assert CLEANUP_EXECUTION_TIMEOUT_HOURS == 1