Intel XEON E-2314 2.80GHZ SKTLGA1200 8.00MB CACHE TRAY

Product Information

CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 3.00 GiB total capacity; 1.92 GiB already allocated; 13.55 MiB free; 1.95 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 CUDA out of memory. Tried to allocate 352.00 MiB (GPU 0; 3.00 GiB total capacity; 1.53 GiB already allocated; 309.83 MiB free; 1.65 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 Desired video size is an approximation value, the file size of output video will be close to this value, it cannot be greater than the source file size. The tool will prompt you if this value is less than 30% of source file size, and you can decide whether to continue. 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

To convert 8 MB to kB we have to multiply the amount in megabytes (MB) by 1000 to get the equivalent in kilobytes (kB). The formula is [kB] = [8] * 1000. Sometimes MByte is used in place of the symbol MB, and the occasionally used term kByte means kB. Therefore, for bytes we get: 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 34.00 MiB (GPU 0; 10.76 GiB total capacity; 1.56 GiB already allocated; 20.75 MiB free; 159.17 MiB cached)CUDA out of memory. Tried to allocate 176.00 MiB (GPU 0; 3.00 GiB total capacity; 1.79 GiB already allocated; 41.55 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 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)” Please make sure the desired video size is not too small (compared to your original file), otherwise the compression may fail. File "/content/gdrive/My Drive/Colab Notebooks/STANet-withpth/models/CDFA_model.py", line 117, in optimize_parameters

proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1) # B X C X (N)/(ds*ds)What is strange, is that the EXACT same code ran fine the first time. When I tried to run the same code with slightly different hyperparams (doesn't affect the model, things like early-stop patience) it breaks during the first few batches of the first epoch. Even when I try to run the same hyperparams as my first experiment, it breaks it is always throwing Cuda out of Memory at different batch sizes, plus I have more free memory than it states that I need, and by lowering batch sizes, it INCREASES the memory it tries to allocate which doesn’t make any sense. CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 3.00 GiB total capacity; 1.86 GiB already allocated; 17.55 MiB free; 1.95 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 tried to run a model on colab and I have this error which seems to be really weird(256.00 GiB !!) same error occurred if I change the data size, the batch size, or clear the GPU memory. If you have been asking yourself is 8 MB smaller than 8 KB, then the answer in any case is “no”. If, on the other hand, you have been wondering is 8 MB bigger than 8 kB, then you now know that this is indeed the case. Conclusion

CUDA out of memory. Tried to allocate 88.00 MiB (GPU 0; 3.00 GiB total capacity; 1.83 GiB already allocated; 9.55 MiB free; 1.96 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 RuntimeError: CUDA out of memory. Tried to allocate 1.12 GiB (GPU 0; 24.00 GiB total capacity; 1.44 GiB already allocated; 19.88 GiB free; 2.10 GiB reserved in total by PyTorch)” CUDA out of memory. Tried to allocate 232.00 MiB (GPU 0; 3.00 GiB total capacity; 1.61 GiB already allocated; 119.55 MiB free; 1.85 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 Also I have found that required memory and allocated memory seem to change with changing the batch sizeBut when run 2 different queries at same time , it gives error like below: Memory limit exceeded: Failed to allocate row batch EXCHANGE_NODE (id=1) could not allocate 8.00 KB without exceeding limit.