Question: I am a beginner at PyTorch and I am just trying out some examples on this webpage. But I can't seem to get the 'super_resolution' program running due to this error: RuntimeError: DataLoader worker (pid(s) 15332) exited unexpectedly I searched the Internet and found that some people suggest setting num_workers to 0. But if I do that, the program tells me that I am running out of memory (either with CPU or GPU): RuntimeError: [enforce fail at ..\c10\core\CPUAllocator.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 9663676416 bytes. Buy new RAM! or RuntimeError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 4.00 GiB total capacity; 2.03 GiB already allocated; 0 bytes free; 2.03 GiB reserved in total by PyTorch) How do I fix this?  I am using python 3.8 on Win10(64bit) and pytorch 1.4.0.  More complete error messages (--cuda means using GPU, --threads x means passing x to the num_worker parameter):  with command line arguments --upscale_factor 1 --cuda   File "E:\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 761, in _try_get_data data = self._data_queue.get(timeout=timeout) File "E:\Python38\lib\multiprocessing\queues.py", line 108, in get raise Empty _queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last): File "Z:\super_resolution\main.py", line 81, in <module> train(epoch) File "Z:\super_resolution\main.py", line 48, in train for iteration, batch in enumerate(training_data_loader, 1): File "E:\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 345, in __next__ data = self._next_data() File "E:\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 841, in _next_data idx, data = self._get_data() File "E:\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 808, in _get_data success, data = self._try_get_data() File "E:\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 774, in _try_get_data raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) RuntimeError: DataLoader worker (pid(s) 16596, 9376, 12756, 9844) exited unexpectedly   with command line arguments --upscale_factor 1 --cuda --threads 0   File "Z:\super_resolution\main.py", line 81, in <module> train(epoch) File "Z:\super_resolution\main.py", line 52, in train loss = criterion(model(input), target) File "E:\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__ result = self.forward(*input, **kwargs) File "Z:\super_resolution\model.py", line 21, in forward x = self.relu(self.conv2(x)) File "E:\Python38\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__ result = self.forward(*input, **kwargs) File "E:\Python38\lib\site-packages\torch\nn\modules\conv.py", line 345, in forward return self.conv2d_forward(input, self.weight) File "E:\Python38\lib\site-packages\torch\nn\modules\conv.py", line 341, in conv2d_forward return F.conv2d(input, weight, self.bias, self.stride, RuntimeError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 4.00 GiB total capacity; 2.03 GiB already allocated; 954.35 MiB free; 2.03 GiB reserved in total by PyTorch) 

Answer: There is no "complete" solve for GPU out of memory errors, but there are quite a few things you can do to relieve the memory demand. Also, make sure that you are not passing the trainset and testset to the GPU at the same time!  Decrease batch size to 1 Decrease the dimensionality of the fully-connected layers (they are the most memory-intensive) (Image data) Apply centre cropping (Image data) Transform RGB data to greyscale (Text data) Truncate input at n chars (which probably won't help that much)  Alternatively, you can try running on Google Colaboratory (12 hour usage limit on K80 GPU) and Next Journal, both of which provide up to 12GB for use, free of charge. Worst case scenario, you might have to conduct training on your CPU. Hope this helps!