Delete device_manager.py

device_manager.py

@@ -1,67 +0,0 @@
-import torch
-import os
-import logging
-import spaces
-from functools import wraps
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-class DeviceManager:
-    _instance = None
-    
-    def __new__(cls):
-        if cls._instance is None:
-            cls._instance = super(DeviceManager, cls).__new__(cls)
-            cls._instance._initialized = False
-        return cls._instance
-    
-    def __init__(self):
-        if self._initialized:
-            return
-        self._initialized = True
-        self.device = self._initialize_device()
-        
-    def _initialize_device(self):
-        """初始化並確定使用的設備"""
-        try:
-            # 檢查是否在 Spaces 環境且有 ZeroGPU
-            if os.environ.get('SPACE_ID') and torch.cuda.is_available():
-                logger.info("ZeroGPU environment detected")
-                return 'cuda'
-        except Exception as e:
-            logger.warning(f"Unable to initialize ZeroGPU: {e}")
-        
-        logger.info("Using CPU")
-        return 'cpu'
-    
-    def get_device(self):
-        """獲取當前設備"""
-        return self.device
-    
-    def to_device(self, model_or_tensor):
-        """將模型或張量移到正確的設備上"""
-        try:
-            if hasattr(model_or_tensor, 'to'):
-                return model_or_tensor.to(self.device)
-        except Exception as e:
-            logger.warning(f"Failed to move to {self.device}, using CPU: {e}")
-            self.device = 'cpu'
-            return model_or_tensor.to('cpu')
-        return model_or_tensor
-
-def adaptive_gpu(duration=60):
-    """結合 spaces.GPU 和 CPU 降級的裝飾器"""
-    def decorator(func):
-        @wraps(func)
-        async def wrapper(*args, **kwargs):
-            device_mgr = DeviceManager()
-            if device_mgr.get_device() == 'cuda':
-                # 在 ZeroGPU 環境中使用 spaces.GPU
-                decorated = spaces.GPU(duration=duration)(func)
-                return await decorated(*args, **kwargs)
-            else:
-                # 在 CPU 環境中直接執行
-                return await func(*args, **kwargs)
-        return wrapper
-    return decorator