Out of memory error when using validation while training a DAGnetwork

Hi,

I'm training a resnet18 netwerk for semantic segmentation with my own data, based on this tutorial: https://nl.mathworks.com/help/vision/examples/semantic-segmentation-using-deep-learning.html .

The training is done on a threadripper 1950x with 64GB of RAM and a gtx1050TI 4GB (i use a minibatchsize of 4 in order for it to fit in the GPU memory). Matlab 2020a is used.

The dataset are images of size [284 481 3], with 10k images in the training dataset and ~3k images in the validation set. Both are stored in a pixelLabelImageDatastore object. When I train the network without validation, memory usage in task manager shows around 6GB used out of 64GB. So no problems there.

However, when I attempt to train the network with validation, memory usage skyrockets to 64GB, with approximately 30 minutes of swapping to disk everytime the validation step happens. After a couple epochs, MATLAB then throws the 'out of memory' error. I even increased the size of the swap file in Windows to 500GB with the same results. It only takes a couple of extra epochs before crashing.

What is the reason this is happing only with the validation data and what can be done to counteract this? I believed that using a datastore made it so that only the data required at a certain moment was read into memory instead of the entire batch? The total file size of all the labeled and input images in my dataset is only ~300MB.

Thanks for any feedback!

Answer 1

[Harsha Priya Daggubati]

Hi,

This is the result of a bug, that leads to GPU's running out of memory as the validation set size grows. This issue is not a result of the increased training set size.

One workaround is to train by splitting the training set into smaller groups (and thus breaking up the validation set), and looping through each of the groups, and having each successive group pick up where the prior group left off.

Another workaround is to reduce the size of the validation set until the GPU does not run out of memory, and continue training on the full training set.

[Anthony Schenck]

Thanks for your reply. However, the issue does not lie with the GPU memory, but with the system memory. The GPU memory usage relates to the minibatchsize setting, and validation data has no effect on it.

In the meantime I've also attempted to train the network on a different system, with 128GB of RAM and an RTX2080ti and here I experienced the same issue, where the entire 128GB of RAM is used, with additional swapping to disk, when validation is selected as an option for training.

As a "workaround" I'm currently training the network without the validation step...

[Brian Derstine]

That seems like a fairly major bug as using an extremely small validation set renders the validation far less useful. Is there a plan to fix this bug?