WebSep 16, 2024 · Your script might be already hitting OOM issues and would call empty_cache internally. You can check it via torch.cuda.memory_stats (). If you see that OOMs were detected, lower the batch size as suggested. antran96 (antran96) September 19, 2024, 6:33am 5 Yes, seems like decreasing the batch size resolve the issue. WebNov 28, 2024 · Out of memory error when resume training even though my GPU is empty vision jdhao (jdhao) November 28, 2024, 10:57am #1 I am training a classification model and I have saved some checkpoints. When I try to resume training, however, I got out of memory errors: Traceback (most recent call last): File “train.py”, line 283, in main ()
How to fix this strange error: "RuntimeError: CUDA error: …
WebMar 16, 2024 · Your problem may be due to fragmentation of your GPU memory.You may want to empty your cached memory used by caching allocator. import torch torch.cuda.empty_cache () Share Improve this answer Follow edited Sep 3, 2024 at 21:09 Elazar 20k 4 44 67 answered Mar 16, 2024 at 14:03 Erol Gelbul 27 3 5 WebMar 5, 2024 · The GPU is a cluster of 4, having cuda takes the 0th ID, which is empty, as well as the first one. So it doesn't really matter which one I use, as long as I annotated all the GPUs the same; 'cuda' or 'cuda:1' – jokkk2312 Mar 6 at 10:32 Add a comment 10 2 3 Know someone who can answer? Share a link to this question via email, Twitter, or Facebook. poms application
How can we release GPU memory cache? - PyTorch Forums
WebDec 15, 2024 · However, the gpu memory will increase gradually and to RuntimeError: CUDA out of memory, even i set batch size=1. I find that although the training gt is less, but the ignore gt is still so many, and according to what @aresgao said, the ignore boxes will be taken into gpu memory to calculate iou, so the gpu memory will still increase and … WebJan 17, 2024 · RuntimeError: CUDA out of memory. Tried to allocate 2.56 GiB (GPU 0; 15.90 GiB total capacity; 10.38 GiB already allocated; 1.83 GiB free; 2.99 GiB cached) I'm trying to understand what this means. WebAug 14, 2024 · These 500MB are most likely just the memory used by the CUDA initialization. So there is not way to remove it unless you kill the process. It seems that the model is only stored in your first process 34296 and the others are using it as expected but just the cuda initialization state is taking a lot of memory shanoah hilliard