Hi @LarryJiang134 ，when I trained the model with train.py,I received this warning:
UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
and this mistake:
Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.49GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
However,my GPU has 8G memory and 6.5G of free memory. Is it true that the program requires 8G+ of memory? Is it possible to train on the basis of not modifying the network?
Hi @LarryJiang134 ，when I trained the model with train.py,I received this warning:
UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "and this mistake:
Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.49GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.However,my GPU has 8G memory and 6.5G of free memory. Is it true that the program requires 8G+ of memory? Is it possible to train on the basis of not modifying the network?