And also make sure that your input picture has a dimension of 512x512. Compression rate does not matter. You can always increase the threshold by increasing the budget limit, but you should look for ways to reduce your initial budgets. You can use source-map-explorer to analyze each and every module in your application and determines what really need and not important to start the application.

It fixed the error for me. It uses about ~3.2GB GPUs when creating a 500x500 image, and ~3.6GB GPUs when creating a 720x1280 image. In my case, I am using GPU RTX 3060, which works only with Cuda version 11.3 or above, and when I installed Cuda 11.3, it came with PyTorch 1.10.1. So I degraded the PyTorch version, and now it is working fine. RuntimeError: CUDA out of memory. Tried to allocate 3.00 GiB (GPU 0; 8.00 GiB total capacity; 3.65 GiB already allocated; 1.18 GiB free; 4.30 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF Tip: MB and KB are usually used to describe the speed of data transfer. Kb/s, also known as kbit/s, refers to kilobit per second (kbps). Mb/s, also called Mbit/s, means megabit per second (Mbps). So, kb/s to MB/s is 0.001 (10-3). That is, 1 Mb/s equals to 1000 kb/s. KB to GB ConversionBoth tensors will allocate 2MB of memory (8 * 8192 * 8 * 4 / 1024**2 = 2.0MB) and the result will use 2.0GB, which would fit your last error message. You could run this code snippet to verify it: a = torch.randn(8, 8192, 8, device='cuda') From the above definition of MB, you can know that 1MB is 1,000,000 (10 6) bytes in the decimal system while 1048576 (2 20) bytes in the binary system. In 1998, the International Electrotechnical Commission (IEC) proposed standards of binary prefixes requiring the use of megabyte to strictly denote 1000 2 (10 6) bytes and mebibyte to denote 1024 2 (2 20) bytes. This proposal was adopted by the IEEE, EU, ISO and NIST by the end of 2009. Yet, the megabyte is still been widely used for decimal and binary systems. Decimal Base Tip: MB can also refer to megabit (Mbit) which is equal to 1,000,000 (106) bit in the decimal system. While in the binary system, there is mebibit; the 1 megabit = 1048576 (220) bit.

A gigabyte is a unit of information or computer storage meaning approximately 1.07 billion bytes. This is the definition commonly used for computer memory and file sizes. Microsoft uses this definition to display hard drive sizes, as do most other operating systems and programs by default.

RuntimeError: CUDA out of memory. Tried to allocate 1.91 GiB (GPU 0; 24.00 GiB total capacity; 894.36 MiB already allocated; 20.94 GiB free; 1.03 GiB reserved in total by PyTorch)” I am currently training a lightweight model on very large amount of textual data (about 70GiB of text). Make sure to add model.to(torch.float16) in load_model_from_config function, just before model.cuda() is called.

help ! RuntimeError: CUDA out of memory. Tried to allocate 1.50 GiB (GPU 0; 10.92 GiB total capacity; 8.62 GiB already allocated; 1.39 GiB free; 8.81 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF 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: Also I have found that required memory and allocated memory seem to change with changing the batch size RuntimeError: CUDA out of memory. Tried to allocate 1.50 GiB (GPU 0; 10.92 GiB total capacity; 8.62 GiB already allocated; 1.39 GiB free; 8.81 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF Tip: Similarly, GB sometimes also refers to gigabit (Gbit) which is one billion bits in a decimal system and there is gibibit equaling to 1073741824 (230) bits in a binary system.Use ng build --prod --build-optimizer. For newer versions, this is done by default with ng build --prod or ng build RuntimeError: CUDA out of memory. 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)” File "/content/gdrive/My Drive/Colab Notebooks/STANet-withpth/models/CDFA_model.py", line 72, in test CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 3.00 GiB total capacity; 1.83 GiB already allocated; 19.54 MiB free; 1.92 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF