We deploy PyTorch models in docker container, which massively increased the size of the docker container by more than 1G.
But when we deploy the model the training has already been done, so technically we don't need to include the machinery involved in training.
We don't even need backpropagation, we just need to run the neural network to get the outputs.
Is it possible to include only part of PyTorch (or another totally different product) that executes a neural network and nothing else? So deployment is light-weight.