Closed GregorKobsik closed 10 months ago
Same Issue on RTX 3090 with 24 GB of VRAM. Working only with a batch size of 8.
updated the environment to an older version (aka Pytorch=1.5.1 and CUDA=10.2).
Running with a batch size of 32 results in an error:
RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 10.75 GiB total capacity; 6.71 GiB already allocated; 1.95 GiB free; 8.02 GiB reserved in total by PyTorch)
P.S.
Managed to run the code on a RTX 2080Ti with a batch size of 31, so I suppose it can be attributed to some inconsistencies with the architecture of the GPU.
Still strange, that a newer version of PyTorch needs so much memory, that I need to reduce the batch size to as low as 8, to be able to run the code.
Hi @SilenKZYoung ,
I currently tried to evaluate your model. Unfortunately, the batch size of 32 does definitely not fit into 11GB of VRAM, not even 16. I could run the training only on a batch size of 8.
I used a RTX 2080Ti. Could you please tell me, how did you fit your Model on a GTX 1080Ti ?
I will try to get my hands on an GTX 1080Ti and try it once again.
ERROR MESSAGE: