This is hell for a lot of ML containers, that have gigabytes of CUDA and PyTorch. Before at least you could keep your code contained to a layer. But if I understand this correctly every code revision duplicates gigabytes of the same damn bloated crap.
If you have problems with 13 (I believe) GB of docker layers ... how do you deal with terabytes or petabytes of AI training data?