Install of PyTorch and CUDA is literally one line in anaconda. I don't remember what I did for tensorflow, but I have it up and running and I am quite the computer phillistine so it could not have been much harder.
Maybe the second part is more of a value prop or good point of focus?
It's value prop is to enable people to easily install TensorFlow / PyTorch and their dependencies in a container-less fashion. Though it doesn't provide the isolation of containers.
What I've learned from talking to customers is that many people don't care that much about handling multiple versions of the same framework. I wouldn't be surprised if you find that, like Lambda Stack, people are mainly using this product to easily get started with TensorFlow/Pytorch.
Now that TensorFlow 2.0 is out, we will see a much more stable API. People won't have to change their code if TensorFlow bumps up a dependency version. For many, this will reduce the impetus for moving to containers.