# Guidelines for assigning num\_workers to DataLoader

**URL:** <https://discuss.pytorch.org/t/guidelines-for-assigning-num-workers-to-dataloader/813>\
**Category:** Uncategorized\
**Created:** [March 1, 2017, 7:03pm UTC](https://discuss.pytorch.org/t/guidelines-for-assigning-num-workers-to-dataloader/813 "2017-03-01T19:03:02Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![YossiB](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/yossib/32/3165_2.png) [@YossiB](https://discuss.pytorch.org/u/YossiB)\
**Post date:** [September 9, 2017, 4:33am UTC](https://discuss.pytorch.org/t/guidelines-for-assigning-num-workers-to-dataloader/813/4 "2017-09-09T04:33:59Z")

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Are you sure that memory usage is the most serious overhead ? What about IO usage ?  
Setting too many workers might cause seriously high IO usage which can become very uneffective.

I would love to get your advice about the recommended way to deal with my data - I feed my CNN with large batches (256/512/1024…) of small patches of size 50x50. I intend to use the ImageFolder DataLoader for that, but I’m afraid that it would be very uneffective to load from disk a lot of small images in high frequency.

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