# ----------- 判断模型是在CPU还是GPU上 ----------------------
 
model = nn.LSTM(input_size=10, hidden_size=4, num_layers=1, batch_first=True)
print(next(model.parameters()).device)  # 输出：cpu
 
model = model.cuda()
print(next(model.parameters()).device)  # 输出：cuda:0
 
model = model.cpu()
print(next(model.parameters()).device)  # 输出：cpu
 
# ----------- 判断数据是在CPU还是GPU上 ----------------------
 
data = torch.ones([2, 3])
print(data.device)  # 输出：cpu
 
data = data.cuda()
print(data.device)  # 输出：cuda:0
 
data = data.cpu()
print(data.device)  # 输出：cpu

方法一

# 如果GPU可用，将模型和张量加载到GPU上
if torch.cuda.is_available():
    model = model.cuda()
    x = x.cuda()
    y = y.cuda()

方法二

# 分配到的GPU或CPU
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 将模型加到GPU
model=model.to(device)
# 将张量加到GPU
x=x.to(device)
y=y.to(device)