即使将模型和数据存储到GPU后，算法也无法在GPU上运行我想念什么？

如何解决即使将模型和数据存储到GPU后，算法也无法在GPU上运行我想念什么？

您可以在下面找到我的代码的培训部分：

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

注意：当我检查torch.cuda.is_available时，我会收到True

创建CNN模型后，我写道：

model = model.to(device)

培训科：

import time
start_time = time.time()

epochs = 3

#Limits on numbers of batches if you want train faster(Not mandatory)
max_trn_batch = 800 # batch 10 image --> 8000 images total
max_tst_batch = 300 # batch 10 image --> 3000 images total

train_losses = []
test_losses = []
train_correct = []
test_correct = []

for i in range(epochs):

trn_corr = 0
tst_corr = 0

for b,(X_train,y_train) in enumerate(train_loader):
    X_train,y_train = X_train.to(device),y_train.to(device)
    
    #optinal limit number of batches
    if b == max_trn_batch:
        break
    b = b + 1
    
    y_pred = model(X_train)
    loss = criterion(y_pred,y_train)
    
    predicted = torch.max(y_pred.data,1)[1]
    batch_corr = (predicted == y_train).sum()
    trn_corr = trn_corr + batch_corr
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if b%200 == 0:
        print('Epoch:  {} Loss:  {} Accuracy:  {}'.format(i,loss,trn_corr.item()*100/(10*b)))

train_losses.append(loss)
train_correct.append(trn_corr)


#test set

with torch.no_grad():
    for b,(X_test,y_test) in enumerate(test_loader):
        X_test,y_test = X_test.to(device),y_test.to(device)
        
        #Optional
        if b==max_tst_batch:
            break
        y_val = model(X_test)
        predicted = torch.max(y_val.data,1)[1]
        batch_corr = (predicted == y_test).sum()
        tst_corr = tst_corr + batch_corr

loss = criterion(y_val,y_test)
test_losses.append(loss)
test_correct.append(tst_corr)
total_time = time.time() - start_time
print(f’Total Time: {total_time/60}) minutes’)

在培训期间，我正在检查CPU和GPU性能，CPU％1时CPU工作％100。

注意：当我将CPU用作设备时，算法花费了13分钟，而当我将GPU用作设备时，算法花费了7分钟，因此似乎没有什么改善，但是在培训期间我看不到任务管理器上的gpu利用率。

注2：参数

ConvolutionalNetwork(
  (conv1): Conv2d(3,6,kernel_size=(3,3),stride=(1,1))
  (conv2): Conv2d(6,16,1))
  (fc1): Linear(in_features=46656,out_features=120,bias=True)
  (fc2): Linear(in_features=120,out_features=84,bias=True)
  (fc3): Linear(in_features=84,out_features=2,bias=True)

）