Implement advanced job scheduling workflow for analyze interface

- Add VRAM requirement estimation for models
- Implement intelligent worker selection based on VRAM, weight, and load
- Add support for concurrent job execution with VRAM tracking
- Integrate RunPod pod creation and management as fallback
- Implement model loading/unloading logic to avoid redundant transfers
- Add file transfer handling for shared storage and websocket fallback
- Update queue system to use advanced cluster scheduling
- Add job_id column to processing_queue table
- Update web interface to submit jobs through queue system
- Fix function name references in cluster master

vidai/cluster_master.py

@@ -335,7 +335,7 @@ class ClusterMaster:
 
     def get_best_worker_for_job(self, process_type: str, model_path: str, job_data: dict = None) -> Optional[str]:
         """Get the best worker for a job considering VRAM requirements, weight, and current load."""
-        from .models import get_model_vram_requirement
+        from .models import estimate_model_vram_requirements
 
         if process_type not in self.process_queue:
             return None
@@ -345,7 +345,7 @@ class ClusterMaster:
             return None
 
         # Get VRAM requirement for the model
-        vram_required = get_model_vram_requirement(model_path)
+        vram_required = estimate_model_vram_requirements(model_path)
 
         # Find workers that can handle this job
         candidate_workers = []
@@ -420,7 +420,7 @@ class ClusterMaster:
 
     async def assign_job_to_worker(self, worker_key: str, job_data: dict) -> Optional[str]:
         """Assign a job to a worker and handle file/model transfer."""
-        from .models import get_model_vram_requirement
+        from .models import estimate_model_vram_requirements
         import uuid
 
         client_id = self.processes[worker_key]['client_id']
@@ -448,7 +448,7 @@ class ClusterMaster:
                 return None
 
         # Track the job
-        vram_required = get_model_vram_requirement(model_path)
+        vram_required = estimate_model_vram_requirements(model_path)
         self.active_jobs[job_id] = {
             'worker_key': worker_key,
             'client_id': client_id,
@@ -718,7 +718,7 @@ class ClusterMaster:
 
     async def assign_job_with_model(self, process_type: str, job_data: dict) -> Optional[str]:
         """Assign a job to a worker with advanced scheduling logic."""
-        from .models import get_model_vram_requirement
+        from .models import estimate_model_vram_requirements
         model_path = job_data.get('model_path', 'Qwen/Qwen2.5-VL-7B-Instruct')
 
         # Step 1: Try to find workers that already have the model loaded
@@ -728,7 +728,7 @@ class ClusterMaster:
             suitable_workers = []
             for worker_key in workers_with_model:
                 available_vram = self._get_worker_available_vram(worker_key)
-                vram_required = get_model_vram_requirement(model_path)
+                vram_required = estimate_model_vram_requirements(model_path)
                 current_jobs = len(self.worker_jobs[worker_key])
                 max_concurrent = self._get_worker_max_concurrent_jobs(worker_key)
 
@@ -770,7 +770,7 @@ class ClusterMaster:
         for proc_key in self.processes:
             if self.processes[proc_key]['name'].startswith(process_type):
                 available_vram = self._get_worker_available_vram(proc_key)
-                vram_required = get_model_vram_requirement(model_path)
+                vram_required = estimate_model_vram_requirements(model_path)
                 current_jobs = len(self.worker_jobs[proc_key])
                 max_concurrent = self._get_worker_max_concurrent_jobs(proc_key)
 
@@ -851,6 +851,70 @@ class ClusterMaster:
 
         return None
 
+    def select_worker_for_job(self, process_type: str, model_path: str, job_data: dict) -> Optional[str]:
+        """Advanced worker selection based on VRAM requirements and job scheduling logic."""
+        from .models import estimate_model_vram_requirements
+
+        # Step 1: Determine VRAM required for the model
+        required_vram_gb = estimate_model_vram_requirements(model_path)
+
+        # Step 2: Determine workers with sufficient GPU memory
+        available_workers = []
+        concurrent_workers = []
+
+        for proc_key, proc_weight in self.process_queue.get(process_type, []):
+            client_id = self.processes[proc_key]['client_id']
+            client_info = self.clients[client_id]
+            gpu_info = client_info['gpu_info']
+
+            # Get total available VRAM
+            total_vram = 0
+            if gpu_info.get('cuda_available'):
+                cuda_devices = gpu_info.get('cuda_device_info', [])
+                total_vram += sum(device.get('vram_gb', 8) for device in cuda_devices)
+            if gpu_info.get('rocm_available'):
+                rocm_devices = gpu_info.get('rocm_device_info', [])
+                total_vram += sum(device.get('vram_gb', 16) for device in rocm_devices)
+
+            # Check if worker has enough VRAM
+            if total_vram >= required_vram_gb:
+                available_workers.append((proc_key, client_info['weight'], total_vram))
+
+        if not available_workers:
+            return None
+
+        # Step 3: Among workers with sufficient memory, select by weight and job status
+        # First priority: workers with higher weight and no running jobs
+        no_job_workers = []
+        for proc_key, weight, vram in available_workers:
+            # Check if worker has running jobs
+            current_jobs = len(self.worker_jobs[proc_key])
+            if current_jobs == 0:
+                no_job_workers.append((proc_key, weight, vram))
+
+        if no_job_workers:
+            # Sort by weight (highest first), then by available VRAM (highest first)
+            no_job_workers.sort(key=lambda x: (x[1], x[2]), reverse=True)
+            return no_job_workers[0][0]
+
+        # Step 4: If no workers with no jobs, check for concurrent job support
+        for proc_key, weight, vram in available_workers:
+            client_id = self.processes[proc_key]['client_id']
+            # Check if worker supports concurrent jobs and has enough free VRAM
+            current_jobs = len(self.worker_jobs[proc_key])
+            max_concurrent = self._get_worker_max_concurrent_jobs(proc_key)
+            current_vram_usage = self.worker_vram_usage[proc_key]
+
+            if current_jobs < max_concurrent and (vram - current_vram_usage) >= required_vram_gb:
+                concurrent_workers.append((proc_key, weight, vram - current_vram_usage))
+
+        if concurrent_workers:
+            # Sort by weight and available VRAM
+            concurrent_workers.sort(key=lambda x: (x[1], x[2]), reverse=True)
+            return concurrent_workers[0][0]
+
+        return None
+
     def disable_process(self, process_key: str) -> bool:
         """Disable a specific process."""
         if process_key in self.processes:

vidai/database.py

@@ -618,6 +618,16 @@ def init_db(conn) -> None:
         # Column might already exist
         pass
 
+    # Add job_id column to processing_queue table if it doesn't exist
+    try:
+        if config['type'] == 'mysql':
+            cursor.execute('ALTER TABLE processing_queue ADD COLUMN job_id VARCHAR(100)')
+        else:
+            cursor.execute('ALTER TABLE processing_queue ADD COLUMN job_id TEXT')
+    except:
+        # Column might already exist
+        pass
+
     # Insert default admin user if not exist
     import hashlib
     default_password = hashlib.sha256('admin'.encode()).hexdigest()

vidai/models.py

@@ -306,3 +306,47 @@ def unload_all_models() -> None:
     for model in _model_cache.values():
         model.unload_model()
     _model_cache.clear()
+
+
+def estimate_model_vram_requirements(model_path: str) -> int:
+    """Estimate VRAM requirements for a model in GB."""
+    model_path_lower = model_path.lower()
+
+    # Vision-language models
+    if 'qwen' in model_path_lower and ('vl' in model_path_lower or 'vision' in model_path_lower):
+        if '7b' in model_path_lower:
+            return 16  # Qwen2.5-VL-7B requires ~16GB VRAM
+        elif '3b' in model_path_lower:
+            return 8   # Qwen2.5-VL-3B requires ~8GB VRAM
+        elif '72b' in model_path_lower:
+            return 144 # Qwen2.5-VL-72B requires ~144GB VRAM
+        else:
+            return 24  # Default for other Qwen VL models
+
+    # Text-only models
+    elif 'llama' in model_path_lower:
+        if '70b' in model_path_lower:
+            return 40
+        elif '65b' in model_path_lower:
+            return 32
+        elif '30b' in model_path_lower:
+            return 16
+        elif '13b' in model_path_lower:
+            return 8
+        elif '7b' in model_path_lower:
+            return 4
+        else:
+            return 16
+
+    elif 'mistral' in model_path_lower:
+        if '7b' in model_path_lower:
+            return 4
+        else:
+            return 8
+
+    elif 'gpt' in model_path_lower or 'chatgpt' in model_path_lower:
+        # These are typically API-based, but if local
+        return 8
+
+    # Default estimate
+    return 8
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