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[Question] How can we train TabNet on large dataset that does not fit in memory? #370

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salman1993 opened this issue Mar 18, 2022 · 1 comment
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[Question] How can we train TabNet on large dataset that does not fit in memory? #370

salman1993 opened this issue Mar 18, 2022 · 1 comment
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@salman1993
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salman1993 commented Mar 18, 2022 •
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Sorry for posting my question here since it's neither a bug or feature request. I would really appreciate some help with my question. Please let me know if there is a better channel to post general questions about the package.

I have a large dataset (e.g. 5 chunks) - each chunk fits in memory but not all 5. What would be the best way to train TabNet model on such a dataset? There might be easier to do this (please thread your comments!) but one idea would be to use PyTorch's IterableDataset. In that case, my question would be if there is anyway to switch out this package's TorchDataset with a TorchIterableDataset?
@salman1993 salman1993 added the enhancement New feature or request label Mar 18, 2022
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There is a similar issue so closing this one

#143

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@salman1993 @Optimox @eduardocarvp