How to fix this strange error: "RuntimeError: CUDA error: out of memory"

I successfully trained the network but got this error during validation:

RuntimeError: CUDA error: out of memory

The error occurs because you ran out of memory on your GPU.

One way to solve it is to reduce the batch size until your code runs without this error.

1.. When you only perform validation not training,
you don't need to calculate gradients for forward and backward phase.
In that situation, your code can be located under

with torch.no_grad():
    ...
    net=Net()
    pred_for_validation=net(input)
    ...

Above code doesn't use GPU memory

2.. If you use += operator in your code,
it can accumulate gradient continuously in your gradient graph.
In that case, you need to use float() like following site
https://pytorch.org/docs/stable/notes/faq.html#my-model-reports-cuda-runtime-error-2-out-of-memory

Even if docs guides with float(), in case of me, item() also worked like

entire_loss=0.0
for i in range(100):
    one_loss=loss_function(prediction,label)
    entire_loss+=one_loss.item()

3.. If you use for loop in training code,
data can be sustained until entire for loop ends.
So, in that case, you can explicitly delete variables after performing optimizer.step()

for one_epoch in range(100):
    ...
    optimizer.step()
    del intermediate_variable1,intermediate_variable2,...