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This repository has been archived by the owner on Oct 31, 2023. It is now read-only.
Thanks for sharing, this is a simple and interesting way to use auxiliary losses.
When using it on a large dataset I get a memory leak, it uses up more and more cuda memory untill it crashes. I think this is because the graph is not cleared loss.backward(retain_graph=True).
The obvious next step is to clear the graph with a loss.backward(retain_graph=False) but I get an error that the variables have been modified (image blow). I assume this is an intentional or metabalance, but I can't find where, and I can't find a way to clear the graph manually.
Any tips?
The text was updated successfully, but these errors were encountered:
I'm going to close this, because this issue seems more complicated than I originally thought. Perhaps it's my torch version or I do have an in-place operation. It's really quite hard to pin down into a clear question I'm afraid.
Thanks for sharing, this is a simple and interesting way to use auxiliary losses.
When using it on a large dataset I get a memory leak, it uses up more and more cuda memory untill it crashes. I think this is because the graph is not cleared
loss.backward(retain_graph=True)
.The obvious next step is to clear the graph with a
loss.backward(retain_graph=False)
but I get an error that the variables have been modified (image blow). I assume this is an intentional or metabalance, but I can't find where, and I can't find a way to clear the graph manually.Any tips?
The text was updated successfully, but these errors were encountered: