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I have a large training set of ~300GB (which is a subset of an even larger dataset ~15TB).

I am trying train a Convnet with Keras (Tensorflow backend) to do something similar to semantic segmentation.

I couldn't find any valuable resources to handle such large data. Any suggestions for best practices for such humungous data is appreciated.

Thanks!

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You don't need to load the whole dataset into memory at once. The only data you need in memory are the samples in a single training batch. Use the fit_generator method rather than fit to pass in an iterator that feeds samples to your model from disk rather than loading all of that data at once. Here's a tutorial that discusses this more.

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  • $\begingroup$ Thanks, a lot! The solution works well for me even though its a bit slow since multiple reads from the disk have to be made during every iteration. $\endgroup$ – Arun Aniyan Mar 8 '18 at 17:04

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