Fix job scheduling: use proper cluster master methods

- Updated queue manager to use select_worker_for_job() and assign_job_to_worker() instead of unimplemented assign_job_with_model()
- Now properly implements VRAM-aware worker selection based on model requirements
- Jobs will be assigned to distributed workers when available with sufficient VRAM
- Falls back to local processing when no suitable distributed worker is found
- Added proper error handling and logging for job assignment process

vidai/queue.py

@@ -129,20 +129,31 @@ class QueueManager:
 
         # Determine process type
         process_type = job['request_type']  # 'analyze' or 'train'
+        model_path = job['data'].get('model_path', 'Qwen/Qwen2.5-VL-7B-Instruct')
 
-        # Use advanced job scheduling
+        # Use advanced job scheduling with VRAM requirements
         try:
-            job_id = cluster_master.assign_job_with_model(process_type, job['data'])
+            worker_key = cluster_master.select_worker_for_job(process_type, model_path, job['data'])
+
+            if worker_key:
+                # Assign job to the selected worker
+                job_id = cluster_master.assign_job_to_worker(worker_key, job['data'])
 
                 if job_id:
                     # Job assigned successfully, mark as processing and store job_id
                     from .database import update_queue_status
                     update_queue_status(job['id'], 'processing', {'job_id': job_id, 'status': 'Assigned to worker'}, job_id=job_id)
+                    print(f"Job {job['id']} assigned to worker {worker_key} with job_id {job_id}")
+                else:
+                    # Failed to assign job, fall back to local processing
+                    print(f"Failed to assign job {job['id']} to worker {worker_key}, falling back to local processing")
+                    self._execute_local_job(job)
             else:
                 # No distributed worker available, fall back to local processing
-                print(f"No distributed worker available for job {job['id']}, falling back to local processing")
+                print(f"No suitable distributed worker available for job {job['id']}, falling back to local processing")
                 self._execute_local_job(job)
         except Exception as e:
+            print(f"Error in distributed job execution for job {job['id']}: {e}")
             from .database import update_queue_status
             update_queue_status(job['id'], 'failed', error_message=str(e))