RuntimeError: CUDA out of memory. Tried to allocate 12.50 MiB (GPU 0; 10.92 GiB total capacity; 8.57 MiB already allocated; 9.28 GiB free; 4.68 MiB cached) #16417
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I have the same runtime error: |
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@EMarquer @OmarBazaraa Could you give a minimal repro example that we can run? |
yf225
added
the
needs reproduction
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I can not reproduce the problem anymore, thus I will close the issue. @OmarBazaraa, I do not think your problem is the same as mine, as:
From my previous experience with this problem, either you do not free the CUDA memory or you try to put too much data on CUDA. |
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Is there any general solution? CUDA out of memory. Tried to allocate 196.00 MiB (GPU 0; 2.00 GiB total capacity; 359.38 MiB already allocated; 192.29 MiB free; 152.37 MiB cached) |
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@aniks23 we are working on a patch that I believe will give better experience in this case. Stay tuned |
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Is there any way to know how big a model or a network my system can handle
without running into this issue?
…On Fri, Feb 1, 2019 at 3:55 AM Francisco Massa ***@***.***> wrote:
@aniks23 <https://github.com/aniks23> we are working on a patch that I
believe will give better experience in this case. Stay tuned
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I also got this message:
It happened when I was trying to run the Fast.ai lesson1 Pets https://course.fast.ai/ (cell 31) |
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I too am running into the same errors. My model was working earlier with the exact setup, but now it's giving this error after I modified some seemingly unrelated code.
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I don't know if my scenario is relatable to the original issue, but I resolved my problem (the OOM error in the previous message went away) by breaking up the nn.Sequential layers in my model, e.g. self.input_layer = nn.Sequential(
nn.Conv3d(num_channels, 32, kernel_size=3, stride=1, padding=0),
nn.BatchNorm3d(32),
nn.ReLU()
)
output = self.input_layer(x)to self.input_conv = nn.Conv3d(num_channels, 32, kernel_size=3, stride=1, padding=0)
self.input_bn = nn.BatchNorm3d(32)
output = F.relu(self.input_bn(self.input_conv(x)))My model has a lot more of these (5 more to be exact). Am I using nn.Sequential right? Or is this a bug? @yf225 @fmassa |
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I am getting a similar error as well:
@treble-maker123 , have you been able to conclusively prove that nn.Sequential is the problem ? |
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I am having a similar issue. I am using the pytorch dataloader. SaysI should have over 5 Gb free but it gives 0 bytes free. RuntimeError Traceback (most recent call last) RuntimeError: CUDA out of memory. Tried to allocate 11.00 MiB (GPU 0; 6.00 GiB total capacity; 448.58 MiB already allocated; 0 bytes free; 942.00 KiB cached) |
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Hi, I also got this error. |
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Sadly, I met the same issue too. I have trained my model in a cluster of servers and the error unpredictably happened to one of my servers. Also such wired error only happens in one of my training strategies. And the only difference is that I modify the code during data augmentation, and make the data preprocess more complicated than others. But I am not sure how to solve this problem. |
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I am also having this issue. How to solve it??? |
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Same issue here |
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@fmassa Do you have any more info on this? |
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The same issue to me warnings.warn(old_gpu_warn % (d, name, major, capability[1])) [05.22.19|12:02:41] Training epoch: 0 |
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It is because of mini-batch of data does not fit on to GPU memory. Just decrease the batch size. When I set batch size = 256 for cifar10 dataset I got the same error; Then I set the batch size = 128, it is solved. |
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Yeah @balcilar is right, I reduced the batch size and now it works |
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I have a similar issue: I am using 8 V100 to train the model. The confusing part is that there is still 3.03GB cached and it cannot be allocated for 11.88MB. |
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Did you change the batch size. Reduce the batch size by half. Say the batch
size is 16 to implement, try using a batch size of 8 and see if it works.
Enjoy
…On Mon, Jun 10, 2019 at 2:10 AM magic282 ***@***.***> wrote:
I have a similar issue:
RuntimeError: CUDA out of memory. Tried to allocate 11.88 MiB (GPU 4; 15.75 GiB total capacity; 10.50 GiB already allocated; 1.88 MiB free; 3.03 GiB cached)
I am using 8 V100 to train the model. The confusing part is that there is
still 3.03GB cached and it cannot be allocated for 11.88MB.
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I tried reducing the batch size and it worked. The confusing part is the error msg that the cached memory is larger than the to be allocated memory. |
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I get the same problem on a pretrained model, when I use predict. So reducing the batch size will not work. |
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If you update to the latest version of PyTorch you might have less errors like that |
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Can I ask why the numbers in the error don't add up?! |
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I have also got the problem when i trained the U-net,the cach is enough ,but it still crash |
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I have the same error... |
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try reducing size (any size that will not change the result) will work. |
Thank you very much! It worked for me! |
how you can do that ? |
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The best way to solve tthis issue is to reduce your batch size, |
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I realised on debugging that my memory was growing in the evaluation phase (validation) and not during training. Apparently, during the validation phase, the intermediate activations are not freed as soon as they are no longer needed, as they are during the training phase. This is because PyTorch's memory management during the forward pass is more aggressive during training, as it can reuse memory for each batch. During validation, however, the activations must be preserved until the entire forward pass is complete, so that the gradients can be computed. Here's how I solved it: I turned off gradient computation during validation: You can set Do not forget to flag it back to |
It works for me! |
It works!! No need to reduce the batch size. Thanks @h-jia ! |
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Hi @AntonG-87, you can write this line in any "test" functions which do not require gradient calculations. You can refer to the official tutorial for more details. |
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I was facing the problem while training, I tried reducing the batch-size, it didn't work. But I noticed while changing my optimizer from Adam to SGD, it works. |
可以尝试运行前将CUDA_VISIBLE_DEVICES设置为1
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@pradyyadav Could you point out where in the code? |
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I had a model training code. So just experimented with different optimizers. |
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i think it might be related to less ram in your system. My ram reached its full capacity before reducing drastially and showing the same error mentioned above. i use 3060 12GB vram and 16 GB ram. |
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Try to restart your computer, it worked for me. |
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Hi, I also have the same problem and I can't solve it. I can't use Animatediff because it then gives me an error. Yet I have 32 GB of DDR4 3600mhz RAM - RTX 4070 OC and Ryzen 7 5800x CPU. |
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I tried to install: Anaconda e |
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I have Quadro RTX 8000 49152MiB GPU I tried from max_split_size_mb:32 to max_split_size_mb: 65536 nothing works. Any one know the fix to this? |
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2024-02-03 20:39:53,872 - Inpaint Anything - ERROR - Allocation on device 0 would exceed allowed memory. (out of memory) |
RuntimeError: CUDA out of memory. Tried to allocate 2.89 GiB (GPU 1; 12.00 GiB total capacity; 8.66 GiB already allocated; 2.05 GiB free; 8.67 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See tambien tengo el mismo error alguien tiene idea de como solucionarlo |
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I am having a similar issue, my batch size is already very small. But I want highlight a detail that many people missed in this post. The OP says: 12.50Mib attempted <<< 9.28 GiB free Many people that commented "I have the same problem" do not have that same exact issue. Also, what about this other part of the message: has it been addressed as part of any solution? If not, does anyone know what is the point of that part of the message? |
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In my case, this was the solution to the memory issue: |
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In my case I just lowered the batch size number from 8 to 4. It worked and the error of "cuda out of memory" was solved. |
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and others
CUDA Out of Memory error but CUDA memory is almost empty
I am currently training a lightweight model on very large amount of textual data (about 70GiB of text).
For that I am using a machine on a cluster ('grele' of the grid5000 cluster network).
I am getting after 3h of training this very strange CUDA Out of Memory error message:
RuntimeError: CUDA out of memory. Tried to allocate 12.50 MiB (GPU 0; 10.92 GiB total capacity; 8.57 MiB already allocated; 9.28 GiB free; 4.68 MiB cached).According to the message, I have the required space but it does not allocate the memory.
Any idea what might cause this ?
For information, my preprocessing relies on
torch.multiprocessing.Queueand an iterator over the lines of my source data to preprocess the data on the fly.Full stacktrace
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