Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,373 Bytes
818a6a6 7a79c1c 818a6a6 c16c12e 818a6a6 c16c12e 818a6a6 c16c12e 818a6a6 c16c12e 818a6a6 c16c12e 818a6a6 c16c12e 818a6a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
from functools import wraps
import torch
from huggingface_hub import HfApi
import os
import logging
import asyncio
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._zero_gpu_available = None
def check_zero_gpu_availability(self):
try:
if 'SPACE_ID' in os.environ:
api = HfApi()
space_info = api.get_space_runtime(os.environ['SPACE_ID'])
if hasattr(space_info, 'hardware') and space_info.hardware.get('zerogpu', False):
self._zero_gpu_available = True
return True
except Exception as e:
logger.warning(f"Error checking ZeroGPU availability: {e}")
self._zero_gpu_available = False
return False
def get_optimal_device(self):
if self._current_device is None:
if self.check_zero_gpu_availability():
try:
self._current_device = torch.device('cuda')
logger.info("Using ZeroGPU")
except Exception as e:
logger.warning(f"Failed to initialize ZeroGPU: {e}")
self._current_device = torch.device('cpu')
else:
self._current_device = torch.device('cpu')
logger.info("Using CPU")
return self._current_device
def device_handler(func):
"""簡化版的 device handler"""
@wraps(func)
async def wrapper(*args, **kwargs):
device_mgr = DeviceManager()
try:
result = await func(*args, **kwargs)
return result
except RuntimeError as e:
if "out of memory" in str(e) or "CUDA" in str(e):
logger.warning("ZeroGPU unavailable, falling back to CPU")
device_mgr._current_device = torch.device('cpu')
return await func(*args, **kwargs)
raise e
return wrapper |