RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 3.94 GiB total capacity; 2.65 GiB already allocated; 2.25 MiB free; 4.11 MiB cached)
See original GitHub issueEpoch: 0
Traceback (most recent call last):
File "main.py", line 142, in <module>
train(epoch)
File "main.py", line 93, in train
outputs = net(inputs)
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/.local/lib/python3.5/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
return self.module(*inputs[0], **kwargs[0])
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/Documents/FL-code/dataset/pytorch-cifar/models/googlenet.py", line 87, in forward
out = self.a4(out)
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/Documents/FL-code/dataset/pytorch-cifar/models/googlenet.py", line 51, in forward
y3 = self.b3(x)
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py", line 338, in forward
self.padding, self.dilation, self.groups)
RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 3.94 GiB total capacity; 2.65 GiB already allocated; 2.25 MiB free; 4.11 MiB cached)
pytorch version: 1.1.0
Issue Analytics
- State:
- Created 4 years ago
- Comments:5
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You just don’t have memory in your GPU. Try to reduce the batch size
I submitted these bugs to PyTorch, and the advice I gave was to adjust the multithreading to 1.