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Got a 3090 here. Attempts to train and immediately get an error coming from PyTorch (tried both 256 and 512 image sizes, but don't think that makes a difference in this case) ::
RuntimeError: CUDA out of memory. Tried to allocate 260.00 MiB (GPU 0; 24.00 GiB total capacity; 21.16 GiB already allocated; 0 bytes free; 21.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Should I do what the runtime error suggests- or would that introduce stability problems?
The text was updated successfully, but these errors were encountered:
I managed to run the larger model using --num_cutouts 24 (default value is 128) and leaving the size untouched.
I'm using a GPU which equips 12 GB of VRAM.
Got a 3090 here. Attempts to train and immediately get an error coming from PyTorch (tried both 256 and 512 image sizes, but don't think that makes a difference in this case) ::
RuntimeError: CUDA out of memory. Tried to allocate 260.00 MiB (GPU 0; 24.00 GiB total capacity; 21.16 GiB already allocated; 0 bytes free; 21.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Should I do what the runtime error suggests- or would that introduce stability problems?
The text was updated successfully, but these errors were encountered: