GPU gets out of memory

1

Hi

batch_size = 100

train = TensorDataset(train_X,train_Y)
trainLoader = DataLoader(train,batch_size, shuffle=True,num_workers=1)

model=model.cuda()

for i,data in enumerate(trainLoader):        
    train_x, train_y = data
    train_x = train_x/255
    #train_x,train_y = Variable(train_x), Variable(train_y)
    train_x = train_x.cuda()
    train_y = train_y.cuda()           
        
    encoded, decoded = model(train_x)

I trained model using the code above successfully.

After training done, I try to get the decoded(output) using the code above same manner.

But my GPU gets out of memory only when computing decoded(output) using trained model.

I don’t know why GPU memory get full although it is trained successfully during training with same code.

2

Are you using the same batch size to compute the new output?
If you don’t need to train the model, wrap the code in a with torch.no_grad() block to save memory.

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3

You may be interested in this topic. There are some discussion here.

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