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Executing train.py in Google Colabo throws two kinds of errors from multiprocessing.
Here, it would be helpful to know whether these errors invalidate the results or are just informative.
Torchmeta is using PyTorch's DataLoader under the hood for data-loading, so this multiprocessing error must come from PyTorch's DataLoader.
I don't know what could cause this issue though unfortunately, but it might be due to Google Colab and how they handle multiprocessing. It might also be related using the synced folder, as in #14.
One solution to prevent this error would be to use --num-workers 0, but that will slow down the data-loading part.
Executing train.py in Google Colabo throws two kinds of errors from multiprocessing.
Here, it would be helpful to know whether these errors invalidate the results or are just informative.
Command executed:
ERROR 1:
ERROR 2:
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