# RuntimeError: \[enforce fail at CPUAllocator.cpp:68\] **URL:** \ **Category:** tensorboard\ **Created:** [January 27, 2022, 9:41am UTC](https://discuss.pytorch.org/t/runtimeerror-enforce-fail-at-cpuallocator-cpp-68/142666 "2022-01-27T09:41:20Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![Samah\_Abu\_saleem](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/samah_abu_saleem/32/43535_2.png) [@Samah\_Abu\_saleem](https://discuss.pytorch.org/u/Samah_Abu_saleem)\ **Post date:** [January 27, 2022, 9:41am UTC](https://discuss.pytorch.org/t/runtimeerror-enforce-fail-at-cpuallocator-cpp-68/142666/1 "2022-01-27T09:41:20Z")
I am new in Pytorch. I got this error when I was trying to train the GCN model on my custom dataset. The error: ```auto --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) /tmp/ipykernel_61/1753868253.py in 30 # Run for 200 epochs (range is exclusive in the upper bound) 31 for epoch in range(1, 201): ---> 32 train() 33 train_acc = test(train_loader) 34 test_acc = test(test_loader) /tmp/ipykernel_61/1753868253.py in train() 10 for data in train_loader: 11 print('x_shape:',data.x.shape) ---> 12 out = model(data.x.float(), data.edge_index, data.batch) 13 loss = criterion(out, data.num_classes) 14 loss.backward() RuntimeError: [enforce fail at CPUAllocator.cpp:68] . DefaultCPUAllocator: can't allocate memory: you tried to allocate 8609587200 bytes. Error code 12 (Cannot allocate memory) ``` My code: ```auto train_loader = DataLoader(trainset, batch_size=64, shuffle=True) test_loader = DataLoader(testset, batch_size=64, shuffle=False) # Define our GCN class as a pytorch Module class GCN(torch.nn.Module): def __init__ (self, hidden_channels): super(GCN, self). __init__ () # We inherit from pytorch geometric's GCN class, and we initialize three layers self.conv1 = GCNConv(data.num_features, hidden_channels) self.conv2 = GCNConv(hidden_channels, hidden_channels) self.conv3 = GCNConv(hidden_channels, hidden_channels) # Our final linear layer will define our output self.lin = Linear(hidden_channels, 8) def forward(self, x, edge_index, batch): # 1. Obtain node embeddings x = self.conv1(x, edge_index) x = x.relu() x = self.conv2(x, edge_index) x = x.relu() x = self.conv3(x, edge_index) # 2. Readout layer x = global_mean_pool(x, batch) # [batch_size, hidden_channels] # 3. Apply a final classifier x = F.dropout(x, p=0.5, training=self.training) x = self.lin(x) return x model = GCN(hidden_channels=64) ``` ---
**Author:** ![InnovArul](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/innovarul/32/5282_2.png) [@InnovArul](https://discuss.pytorch.org/u/InnovArul)\ **Post date:** [January 27, 2022, 3:16pm UTC](https://discuss.pytorch.org/t/runtimeerror-enforce-fail-at-cpuallocator-cpp-68/142666/2 "2022-01-27T15:16:58Z")
> [@Samah\_Abu\_saleem](#): > > `DefaultCPUAllocator: can't allocate memory: you tried to allocate 8609587200 bytes.` The error message seems clear? That your model is needing more memory than the GPU memory size? ---
**Author:** ![lesnikow](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/l/eb8c5e/32.png) [@lesnikow](https://discuss.pytorch.org/u/lesnikow)\ **Post date:** [March 12, 2022, 6:41pm UTC](https://discuss.pytorch.org/t/runtimeerror-enforce-fail-at-cpuallocator-cpp-68/142666/3 "2022-03-12T18:41:18Z")
`DefaultCPUAllocator` is not the same as e.g. `DefaultCUDAAllocator`.