I'm working on training a neural network on an image dataset. There are 14k images and each image contains 3x150x150 pixels. I have built a generator function following the approach in Training on Large Datasets for Out-of-Core training. According to the reference

The first approach is for users to write a generator function f that, when evaluated, can load a single batch of data from an external source such as a disk or database. NetTrain[net,f,…] calls f at each training batch iteration, thus only keeping a single batch of training data in memory.

I use a generator function in that documentation:

genTrain = Function[RandomSample[trainingData, #BatchSize]]

The output of it is as expected:


However, the memory consumption of NetTrain keeps growing every round. I use a small RoundLength here in case of memory exhaust:

trainedNet = NetTrain[lenet, {genTrain, "RoundLength" -> 280}, 
TargetDevice -> "GPU", MaxTrainingRounds -> 50, BatchSize -> 64, 
WorkingPrecision -> "Mixed", PerformanceGoal -> "TrainingMemory"]

Here is the memory usage from the task manager:


It increases till the end.

I've tried $HistoryLength=0, Module[{x=Function[RandomSample[trainingData, #BatchSize]]},x] but nothing changed.

My questions are:

  1. Am I getting the right idea about Out-of-Core training? It should load a single batch of data and free it after use, isn't it? Or is there a bug?

  2. What can I do to decrease memory consumption, for example, free the memory after every round or batch?

  • 5
    $\begingroup$ This is a bug (memory leak) in NetEncoder["Image"] when the inputs are files. The bug is present in 12.3 and 13.0. It was fixed in 13.0.1. $\endgroup$ Commented Feb 18, 2022 at 5:32
  • $\begingroup$ That development environment is used with such nice colors? $\endgroup$
    – Andrew
    Commented Feb 18, 2022 at 7:05
  • 2
    $\begingroup$ @Andrew, check this draculatheme.com/wolfram-notebooks $\endgroup$ Commented Feb 18, 2022 at 12:00
  • 1
    $\begingroup$ Use ResourceFunction["DarkMode"][] $\endgroup$
    – user5601
    Commented Feb 20, 2022 at 1:59

1 Answer 1


As Charmbracelet mentioned in the comments, this bug was fixed in 13.0.1 but just in case it's useful here is a way to work around it based on the generator approach you mentioned.

LoadTrainingPair[idx_] := Module[{out},
  out = <|"Input" -> 
    "Output" -> 

You can see the full story + example notebook with some helpful feedback from Wolfram folks on the community forum (in my case I was working on per-pixel segmentation so the memory leakage was twice as bad).


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.