May 24, 2020 · RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 15.90 GiB total capacity; 2.13 GiB already allocated; 19.88 MiB free; 2.14 GiB reserved in total by PyTorch) Kindly help me with this
Jan 03, 2022 · This answer is useful. 0. This answer is not useful. Show activity on this post. There are 2 possible causes : (Most likely) you forget to use detach () after backpropagating the loss with loss.backward () loss.backward () -------> loss.detach () You have a problem with you CUDA or your computer is using GPU for another task.
I have not encountered this when running smaller models, but when I try to deepen the model by one layer, this happens and instead of CUDA out of memory at the beginning of training, I get an error at some epoch, which I am confused about. After update, the detailed log information is posted in the Additional context section.
10.06.2020 · I'm currently training on a very very large dataset with 4 GPUs and I get a CUDA out of memory error after the completion of 1 training epoch. After the training is complete, when validation starts, it runs out of memory. Here is the exact message:
02.04.2020 · RuntimeError: CUDA out of memory. after the first epoch with custom dataset - TTS (Text-to-Speech) - Mozilla Discourse I’m trying to train a model using a custom dataset but I get a CUDA out of memory error after the first epoch. I’m able to train a model using LJSpeech fine. I’ve tried reducing the batch size from 32 to 16 to 8 all th…
Implementing gradient accumulation and automatic mixed precision to solve CUDA out of memory issue when training big deep learning models which requires ...
16.09.2020 · When I run torch.cuda.memory_cached () after the end of each epoch, my memory cached is unchanged at 3.04GB (like every digit is the same), which is weird to me but I still get CUDA out of memory and the cached memory is >10GB? ptrblck September 18, 2020, 11:13pm #4
08.11.2018 · It looks like you are directly appending the training loss to train_loss [i+1], which might hold a reference to the computation graph. If that’s the case, you are storing the computation graph in each epoch, which will grow your memory. You need to detach the loss from the computation, so that the graph can be cleared. Change this line of code to:
21.01.2020 · The usual reason this happens is: You accumulate your loss (for later printing) in a differentiable manner like all_loss += loss. This means that all_loss keeps all the history of all the previous iterations. You can fix it by doing all_loss += loss.item () to get a python number that does not track gradients.
Feb 11, 2022 · This might point to a memory increase in each iteration, which might not be causing the OOM anymore, if you are reducing the number of iterations. Check the memory usage in your code e.g. via torch.cuda.memory_summary () or torch.cuda.memory_allocated () inside the training iterations and try to narrow down where the increase happens (you ...
11.02.2022 · Check the memory usage in your code e.g. via torch.cuda.memory_summary () or torch.cuda.memory_allocated () inside the training iterations and try to narrow down where the increase happens (you should also see that e.g. loss.backward () reduces the memory usage).
Nov 08, 2018 · It looks like you are directly appending the training loss to train_loss [i+1], which might hold a reference to the computation graph. If that’s the case, you are storing the computation graph in each epoch, which will grow your memory. You need to detach the loss from the computation, so that the graph can be cleared. Change this line of code to:
24.05.2020 · RuntimeError: CUDA out of memory after some epochs #510. Closed anirbansen3027 opened this issue May 24, 2020 · 5 comments Closed ... RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 15.90 …
Dec 27, 2018 · In case PyTorch isn't releasing GPU memory, try manually deleting the CUDA variables using .delete() on them at the end of each epoch. – akshayk07 Dec 29, 2018 at 19:16
01.06.2020 · Following up from #79, instead of getting stuck on evaluation anymore (yay), it now reports a CUDA out of memory error after running the first epoch: RuntimeError ...
Jun 10, 2020 · I'm currently training on a very very large dataset with 4 GPUs and I get a CUDA out of memory error after the completion of 1 training epoch. After the training is complete, when validation starts, it runs out of memory. Here is the exact message:
26.12.2018 · Here is my code. I am receiving CUDA out of memory error after executing 140 batches successfully. I have used item method to avoid storing tensor. Used empty_cache, gc collect, retain_graph as False while calling backward. But all in vain. Kindly suggest.
So reducing the batch_size after restarting the kernel and finding the optimum batch_size is the best possible option (but sometimes not a very feasible one).