I believe that most of the friends who use pytorch to run programs have encountered this problem on the server:
run out of memory, in fact, it means that there is not enough memory.
1. When the bug prompt specifically indicates how much memory a certain gpu has used, the remaining memory is not enough
In this case, only
batch_size needs to be reduced
2. No matter how you adjust the
batch_size, an error will still be reported: run out of memory
This situation is because your pytorch version is too high, add the following code at this time
with torch.no_grad(): output = net(input,inputcoord)
3. If there is no indication of how much memory has been used and how much memory is left
At this time, it may be because your pytorch version does not match the cuda version, then you can enter the following code in your terminal command line:
import torch print(torch.__version__) print(torch.version.cuda) print(torch.backends.cudnn.version()) print(torch.cuda.is_available())
Through the above code, you can see your current torch version, cuda version, cudnn version, and whether torch can use gpu under the current cuda version. If it returns false, it is recommended to adjust the cuda version or the pytorch version.