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 28.00 MiB (GPU 0; 24.00 GiB total capacity; 2.78 GiB already allocated; 19.15 GiB free; 2.82 GiB reserved in total by PyTorch)” 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)”

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

User Comments :

RuntimeError: CUDA out of memory. Tried to allocate 256.00 GiB (GPU 0; 14.76 GiB total capacity; 824.42 MiB already allocated; 11.68 GiB free; 1.80 GiB reserved in total by PyTorch) 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. base_lr': 0.1, 'ignore_weights': [], 'model': 'net.st_gcn.Model', 'eval_interval': 5, 'weight_decay': 0.0001, 'work_dir': './work_dir', 'save_interval': 10, 'model_args': {'in_channels': 3, 'dropout': 0.5, 'num_class': 60, 'edge_importance_weighting': True, 'graph_args': {'strategy': 'spatial', 'layout': 'ntu-rgb+d'}}, 'debug': False, 'pavi_log': False, 'save_result': False, 'config': 'config/st_gcn/ntu-xsub/train.yaml', 'optimizer': 'SGD', 'weights': None, 'num_epoch': 80, 'batch_size': 64, 'show_topk': [1, 5], 'test_batch_size': 64, 'step': [10, 50], 'use_gpu': True, 'phase': 'train', 'print_log': True, 'log_interval': 100, 'feeder': 'feeder.feeder.Feeder', 'start_epoch': 0, 'nesterov': True, 'device': [0], 'save_log': True, 'test_feeder_args': {'data_path': './data/NTU-RGB-D/xsub/val_data.npy', 'label_path': './data/NTU-RGB-D/xsub/val_label.pkl'}, 'train_feeder_args': {'data_path': './data/NTU-RGB-D/xsub/train_data.npy', 'debug': False, 'label_path': './data/NTU-RGB-D/xsub/train_label.pkl'}, 'num_worker': 4}

RuntimeError: CUDA out of memory. Tried to allocate 344.00 MiB (GPU 0; 24.00 GiB total capacity; 2.30 GiB already allocated; 19.38 GiB free; 2.59 GiB reserved in total by PyTorch)” 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 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. I get that not everyone will have the capability to create images with these settings, but after making the changes above I have not run into any more CUDA errors, even when changing the to as high as 3 CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 3.00 GiB total capacity; 1.87 GiB already allocated; 5.55 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_CONFFor the batch size I have applied first 20 possible squares of twos [2, 4, 16, 34 . . . 1048576] yet I have been getting error 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; 1.87 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 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 Also at the end of the FIRST section of my positive prompts (I say first section because you should break your prompts up into 5 sections), I always add "DLSS, Ray Tracing, uncensored, --n_samples 1"

CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 3.00 GiB total capacity; 1.83 GiB already allocated; 27.55 MiB free; 1.94 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_CONFThe answer to the question how much kB in 8 MB is usually 8000 kB, but depending on the vendor of the RAM, hard disk, the software producer or the CPU manufacturer for example, MB could also mean 1024 * 1024 B = 1024 2 bytes. Even a mixed use 1000 * 1024 B cannot completely ruled out. Unless indicated differently, go with 8 MB equal 8000 kB. My computer also has 32 GB ram and CPU synthesis working very well but just too slow. In 7 hours processed only 1 hour of speech. File "/home/linmin001/megan_0/src/utils/dispatch_utils.py", line 187, in dispatch_configurable_command