Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,974 Bytes
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from functools import wraps
import torch
import os
import logging
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._current_device = None
self.initialize_zero_gpu()
def initialize_zero_gpu(self):
"""初始化 ZeroGPU"""
try:
# 檢查是否在 Hugging Face Spaces 環境中
if os.environ.get('SPACE_ID'):
# 嘗試初始化 ZeroGPU
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# 設置必要的環境變數
os.environ['ZERO_GPU'] = '1'
logger.info("ZeroGPU environment initialized")
except Exception as e:
logger.warning(f"Failed to initialize ZeroGPU environment: {e}")
def check_zero_gpu_availability(self):
"""檢查 ZeroGPU 是否可用"""
try:
if os.environ.get('SPACE_ID') and os.environ.get('ZERO_GPU') == '1':
# 確保 CUDA 運行時環境正確設置
if torch.cuda.is_available():
torch.cuda.init()
return True
except Exception as e:
logger.warning(f"ZeroGPU check failed: {e}")
return False
def get_optimal_device(self):
"""獲取最佳可用設備"""
if self._current_device is None:
if self.check_zero_gpu_availability():
try:
self._current_device = torch.device('cuda')
logger.info("Using ZeroGPU")
# 嘗試進行一次小規模的 CUDA 操作來驗證
torch.zeros(1).cuda()
except Exception as e:
logger.warning(f"Failed to use ZeroGPU: {e}")
self._current_device = torch.device('cpu')
logger.info("Fallback to CPU")
else:
self._current_device = torch.device('cpu')
logger.info("Using CPU (ZeroGPU not available)")
return self._current_device
def move_to_device(self, tensor_or_model):
"""將張量或模型移動到最佳設備"""
device = self.get_optimal_device()
try:
if hasattr(tensor_or_model, 'to'):
return tensor_or_model.to(device)
except Exception as e:
logger.warning(f"Failed to move to {device}, falling back to CPU: {e}")
self._current_device = torch.device('cpu')
if hasattr(tensor_or_model, 'to'):
return tensor_or_model.to('cpu')
return tensor_or_model |