![]() ![]() Tried to allocate 1.91 GiB (GPU 0 24.00 GiB total capacity 894.36 MiB already allocated 20.94 GiB free 1. Tried to allocate 1.12 GiB (GPU 0 24.00 GiB total capacity 1.44 GiB already allocated 19.88 GiB free 2.10 GiB reserved in total by PyTorch)” Tried to allocate 482.00 MiB (GPU 0 24.00 GiB total capacity 2.21 GiB already allocated 19.48 GiB free 2.50 GiB reserved in total by PyTorch)” Reduced batch size but tried to allocate more ? Tried to allocate 344.00 MiB (GPU 0 24.00 GiB total capacity 2.30 GiB already allocated 19.38 GiB free 2.59 GiB reserved in total by PyTorch)” Tried to allocate 28.00 MiB (GPU 0 24.00 GiB total capacity 2.78 GiB already allocated 19.15 GiB free 2.82 GiB reserved in total by PyTorch)” Is says it tried to allocate 3.12GB and I have 19GB free and it throws an error? FAQ Small tool for exact determination of the CPU frequency. Tried to allocate 3.12 GiB (GPU 0 24.00 GiB total capacity 2.06 GiB already allocated 19.66 GiB free 2.31 GiB reserved in total by PyTorch)” “RuntimeError: CUDA error: out of memory”.It is always throwing Cuda out of Memory at different batch sizes, plus I have more free memory than it states that I need, and by lowering batch sizes, it INCREASES the memory it tries to allocate which doesn’t make any sense. I am using a 24GB Titan RTX and I am using it for an image segmentation Unet with Pytorch, EDIT: SOLVED - it was a number of workers problems, solved it by lowering them ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |