Classify/NetTrain run indefinitely without output when using GPU

Question

I am experimenting with Mathematica's Classify/NetTrain functions for the first time. When I don't set a TargetDevice option, Mathematica defaults to the CPU and everything works as expected. When I specify "TargetDevice"->"GPU" and run the same code, the kernel runs the code seemingly indefinitely and I can't interrupt the process without quitting the kernel.

I do get this error whenever I use function using "NeuralNetwork" for the first time:

General::newmxnet: An unexpected condition happened: a neural network runtime of the wrong version was loaded. This might lead to unexpected problems. Please contact Wolfram Research.

afaik, all my GPU drivers are up to date and I have tried running functions with and without loading the CudaLink package.

The code I am using is pretty straight forward, I don't think the problem is there, but I'll post it anyway in case it is:

This works:

Classify[
    trainingset,
    Method -> "NeuralNetwork"
 ]

Classify[
     trainingset,
     Method -> "NeuralNetwork",
     TargetDevice -> "CPU"
     ]

This does not:

Classify[
     trainingset,
     Method -> "NeuralNetwork",
     TargetDevice -> "GPU"
     ]

I hope someone can help me out.

EDIT: After letting the NetTrain function run for multiple hours, the process finally exits with the following error code:

NetTrain::arrdiv: Training was stopped early because one or more trainable parameters of the net diverged. The net with the lowest validation loss will be returned. To avoid divergence, ensure that the training data has been normalized to have zero mean and unit variance. You can also try specifying a lower learning rate, or use a different optimization method; the (possibly automatic) values used were Method->{ADAM,Beta1->0.9,Beta2->0.999,Epsilon->1/100000,GradientClipping->None,L2Regularization->None,LearningRate->Automatic,LearningRateSchedule->None,WeightClipping->None}, LearningRate->0.001. Alternatively, you can use the "GradientClipping" option to Method to bound the magnitude of gradients during training.