Load multiple Files from fileDatastore in minibatchqueue

When training my neural network, I noticed that approximately 50% of the runtime is spent on loading data. Currently, the data loading process from my minibatchqueue is sequential and involves numerous function calls. Specifically, each minibatch requires 256 individual function calls, with each file loaded one after another, causing significant delays.

My goal is to parallelize or otherwise optimize this data-loading process to significantly reduce the runtime.

I'm looking for recommendations or best practices to adress the issue.

Any suggestions or ideas for enhancing data-loading efficiency would be greatly appreciated.

the path is similar to this: "C:/user/me/data/s*/spec.mat"

% Create Datastore
fdsNetInput = fileDatastore(path, ReadFcn=@loadDatastoreData, FileExtensions=".mat");

% add labels and create trainSet (not the core of my Question)
fullData = combine(fdsNetInput, fdsLabel);
trainMask = trainMask(randperm(N));
trainData = subset(fullData, trainMask);

my ReanFcn:

function [netInput] = loadDatastoreData(file)
    % Load Spectrum
    netInputSpectrum = load(file, "spectrum"); %<-------------------------- This Line takes a lot of time
    
    % scale Spectrum to the right size
    netInputSpectrum = scaleData(netInputSpectrum, "VGGish", false);
    
    % ouput Spectrum with the propper dims
    netInput = dlarray(netInputSpectrum, 'SSC');
end

then I create a minibatchqueue with the fileDatastore

% create train minibatchqueue
trainMBQ = minibatchqueue(trainData,...
    MiniBatchFormat = ["SSCB", "CB"], ...
    MiniBatchSize = 256, ...
    PartialMiniBatch="discard", ...
    OutputEnvironment="gpu");

Read the Data from the minibatchqueue to process it.

% Read mini-batch of data.
[input,target] = next(trainMBQ);

Answer 1

[Joss Knight]

Does the training option PreprocessingEnvironment set to 'background' not work for you? Or is this a custom training loop?

[Simon]

Thank you for your fast Answer.

it is a custom training loop and I added PreprocessingEnvironment="parallel" in the Minibatchqueue, witch inproved the runtime a lot.

trainMBQ = minibatchqueue(trainData,...
    MiniBatchFormat = ["SSCB", "CB"], ...
    MiniBatchSize = minibatchSize, ...
    PartialMiniBatch="discard", ...
    PreprocessingEnvironment="parallel", ...
    OutputEnvironment="gpu");

[Joss Knight]

Great! "background" should hopefully also work and be faster.