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I've been trying to train an RNN on some data that I pre-processed externally and saved to disk. My dataset is too large to fit in memory and is additionally varying in shape.

Looking for a way to do out-of-core training with such requirements, the closest thing I've found was this question, however it's not quite what I need.

So, my question is whether or not I can create custom NetEncoder (i.e. something I can pass into my NetGraph). Or if this is a feature being worked on?

Otherwise, is my only option to load subsets of my data for training?

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The most general way of doing out-of-core training is to use the 5th usage of NetTrain (NetTrain[net, f]). You can write a custom data loader/sampler f that reads from disk (make sure its fast enough though!).

I would also recommend this tutorial to anyone thinking of doing out-of-core training: http://reference.wolfram.com/language/tutorial/NeuralNetworksLargeDatasets.html

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