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After the change to avoid using memmap when loading the labels #2081, we're encountering pickling errors via numpy.
We're unable to load the labels with numpy.load. The previous loading via np.memmap (be6ff52) did work on our environments without issues
Was the previous mode of loading the labels incorrect leading to incorrect labels? Or the current modification fixes a previously missed bug? If the latest change doesn't concern correctness of loaded labels, can we bring back the old way so as the default, and switch to np.load if --no-memmap is passed?
The text was updated successfully, but these errors were encountered:
After the change to avoid using memmap when loading the labels #2081, we're encountering pickling errors via numpy.
We're unable to load the labels with
numpy.load
. The previous loading vianp.memmap
(be6ff52) did work on our environments without issuesSteps to reproduce:
wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper/node_label_2K.npy
python -c "import numpy as np; labels = np.load('node_label_2K.npy', mmap_mode=None)"
ValueError: Cannot load file containing pickled data when allow_pickle=False
After setting
allow_pickle=True
, we face another error:_pickle.UnpicklingError: invalid load key, '\x00'
Packages used:
numpy==1.26.4
,torch==2.1.0+cpu
Curious if there's a dependency issue on my side.
cc: @arjunsuresh @nv-alicheng
Was the previous mode of loading the labels incorrect leading to incorrect labels? Or the current modification fixes a previously missed bug? If the latest change doesn't concern correctness of loaded labels, can we bring back the old way so as the default, and switch to
np.load
if--no-memmap
is passed?The text was updated successfully, but these errors were encountered: