Comments (4)
It's because torch.nn.functions.conv
imports torch.backends.cudnn
, which imports torch.cuda
every time and never catches an exception if it's not available. Dataparallel should have nothing to do with it.
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nn/parallel/functions.py line 1, 2
I fixed the cudnn issues locally, and this was the next problematic line (included from test_nn.py line 9).
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right, we shouldn't include parallel if we're not using CUDA
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i'm sending a fix for these.
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