原理

.to(device) 可以指定CPU 或者GPU

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # 单GPU或者CPU model.to(device) #如果是多GPU if torch.cuda.device_count() > 1: model = nn.DataParallel(model，device_ids=[0,1,2]) model.to(device)

.cuda() 只能指定GPU

#指定某个GPU os.environ['CUDA_VISIBLE_DEVICE']='1' model.cuda() #如果是多GPU os.environment['CUDA_VISIBLE_DEVICES'] = '0,1,2,3' device_ids = [0,1,2,3] net = torch.nn.Dataparallel(net, device_ids =device_ids) net = torch.nn.Dataparallel(net) # 默认使用所有的device_ids net = net.cuda() class DataParallel(Module): def __init__(self, module, device_ids=None, output_device=None, dim=0): super(DataParallel, self).__init__() if not torch.cuda.is_available(): self.module = module self.device_ids = [] return if device_ids is None: device_ids = list(range(torch.cuda.device_count())) if output_device is None: output_device = device_ids[0]