Update #2
I set up a conda environment in WSL with the pytorch-directml package and downloaded the sample repo provided by Microsoft. The pytorch-directml package requires python 3.8, and the sample repo uses torchvision 0.9.0.
I was able to train a ResNet50 model using the sample training script. GPU memory usage was volatile when using a batch size higher than 4. The ResNet50 training script used about 3.6 GB of GPU memory at a batch size of 4 but spikes to nearly using all 16 GB at a batch size of 8.
I then attempted to train the style transfer model included with the pytorch examples repo and hit the wall of unimplemented operators.
For those curious, here is the PyTorch DirectML Operator Roadmap. There is some basic stuff missing still.