Aug 07, 2020 · It may be due to your retaining all the graphs of all iterations when you do res=res - output. Can you try res = res - output.detach()? Also, can you monitor the memory usage of your gpu (with nvidia-smi -l 1 for example) and check whether the memory usage increases linearly with iterations ? –
My problem: Cuda out of memory after 10 iterations of one epoch. (It made me think that after an iteration I lose track of cuda variables which surprisingly were not collected by garbage collector) Solution: Delete cuda variables manually (del variable_name) after each iteration. 2. level 1. …
09.10.2019 · 🐛 Bug Sometimes, PyTorch does not free memory after a CUDA out of memory exception. To Reproduce Consider the following function: import torch def oom(): try: x = torch.randn(100, 10000, device=1) for i in range(100): l = torch.nn.Linear...
13.08.2009 · Hello All, Im using 8400GS GPU card with CUDA2.2. Below is my project structure and using gpu memory… After some iterations in StartCompute() function cudaMalloc() is returning cudaErrorMemoryAllocation. …
21.01.2020 · Hey, My training is crashing due to a ‘CUDA out of memory’ error, except that it happens at the 8th epoch. In my understanding unless there is a memory leak or unless I am writing data to the GPU that is not deleted every epoch the CUDA memory usage should not increase as training progresses, and if the model is too large to fit on the GPU then it should not pass the first …
19.01.2017 · Why don't you run your simulation and monitor GPU memory in a separate terminal or command window using nvidia-smi, something like: Theme. Copy. nvidia-smi -l 1 -q -d MEMORY. If memory usage is continually going up then you've got some sort of problem with your simulation not releasing variables.
22.07.2021 · I want to run some experiments on my GPU device, but I get this error: RuntimeError: CUDA out of memory. Tried to allocate 3.63 GiB (GPU 0; 15.90 GiB total capacity; 13.65 GiB already allocated; 1...
08.10.2021 · I wonder whether I can change any config of the model to descrease the size memory of CUDA. Thank you so much for being so attentive. The text was updated successfully, but these errors were encountered:
Oct 08, 2021 · I wonder whether I can change any config of the model to descrease the size memory of CUDA. Thank you so much for being so attentive. The text was updated successfully, but these errors were encountered:
Jul 22, 2021 · As long as a single sample can fit into GPU memory, you do not have to reduce the effective batch size: you can do gradient accumulation.Instead of updating the weights after every iteration (based on gradients computed from a too-small mini-batch) you can accumulate the gradients for several mini-batches and only when seeing enough examples, only then updating the weights.
29.02.2020 · Dear All, I run cublasSgemv(…) on the same inputs n times in a loop and print the performance of each run of cublasSgemv(…). The performance is relatively stable per run for the first 1036 runs. Then the performance becomes around 100x worse, suddenly and stays stabil around the new, worse value from there on. Nothing else is done in the loop! The iteration # at …
Dec 16, 2020 · Yes, these ideas are not necessarily for solving the out of CUDA memory issue, but while applying these techniques, there was a well noticeable amount decrease in time for training, and helped me to get ahead by 3 training epochs where each epoch was approximately taking over 25 minutes.
30.11.2019 · This gives a readable summary of memory allocation and allows you to figure the reason of CUDA running out of memory. I printed out the results of the torch.cuda.memory_summary() call, but there doesn't seem to be anything informative that would lead to a fix. I see rows for Allocated memory, Active memory, GPU reserved memory, etc.