# Loading huge data functionality

**URL:** <https://discuss.pytorch.org/t/loading-huge-data-functionality/346>\
**Category:** Uncategorized\
**Created:** [February 5, 2017, 3:46am UTC](https://discuss.pytorch.org/t/loading-huge-data-functionality/346 "2017-02-05T03:46:02Z")\
**Posts on this page:** 1\
**Showing post:** 30

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**Author:** ![traviskaufman](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/traviskaufman/32/8141_2.png) [@traviskaufman](https://discuss.pytorch.org/u/traviskaufman)\
**Post date:** [December 14, 2018, 1:57pm UTC](https://discuss.pytorch.org/t/loading-huge-data-functionality/346/30 "2018-12-14T13:57:20Z")

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Out of curiosity, have you tried using [ConcatDataset](https://pytorch.org/docs/0.3.0/data.html#torch.utils.data.ConcatDataset)? I noticed that seemed to be the answer in a [similar question](https://discuss.pytorch.org/t/dataloaders-multiple-files-and-multiple-rows-per-column-with-lazy-evaluation/11769/2?u=traviskaufman) someone had.

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