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

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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 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 Hi @AntonG-87, you can write this line in any "test" functions which do not require gradient calculations. RuntimeError: CUDA out of memory. Tried to allocate 1.50 GiB (GPU 0; 8.00 GiB total capacity; 5.62 GiB already allocated; 0 bytes free; 5.74 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 File "/content/gdrive/My Drive/Colab Notebooks/STANet-withpth/models/CDFA_model.py", line 90, in forward

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)” It turned out that the cause of this issue was TensorFlow imported alongside with PyTorch. It seems that TensorFlow allocates all the free memory right after it's being imported.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) And also make sure that your input picture has a dimension of 512x512. Compression rate does not matter. I ran it via project stable-diffusion-webui and set the environment variable in webui-macos-env.sh or webui-user.bat.

Since 1999, the IEC recommends that this unit should instead be called a "gibibyte" (abbreviated GiB). The difference between units based on SI and binary prefixes increases exponentially — in other words, an SI kilobyte is nearly 98% as much as a kibibyte, but a megabyte is under 96% as much as a mebibyte, and a gigabyte is just over 93% as much as a gibibyte. This means that a 500 GB hard disk drive would appear as "465 GB". RuntimeError: CUDA out of memory. Tried to allocate 26.11 GiB (GPU 0; 23.70 GiB total capacity; 4.31 GiB already allocated; 16.35 GiB free; 5.03 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_CONFCUDA 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

I am trying to run a pytorch code with jupyter notebook and I got this error RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 2.00 GiB total capacity; 1.23 GiB already allocated; 18.83 MiB free; 1.25 GiB reserved in total by PyTorch) File "/home/emarquer/miniconda3/envs/pytorch/lib/python3.6/runpy.py", line 193, in _run_module_as_mainRuntimeError: CUDA out of memory. Tried to allocate 70.00 MiB (GPU 0; 4.00 GiB total capacity; 2.87 GiB already allocated; 0 bytes free; 2.88 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 482.00 MiB (GPU 0; 24.00 GiB total capacity; 2.21 GiB already allocated; 19.48 GiB free; 2.50 GiB reserved in total by PyTorch)” RuntimeError: CUDA out of memory. Tried to allocate 32.75 MiB (GPU 0; 4.93 GiB total capacity; 3.85 GiB already allocated; 29.69 MiB free; 332.48 MiB cached)

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