PyTorch is too heavy for deployment, is there a light weight pytorch for running neural networks?
One thing to try is to create a lean version of Pytorch that only has what you need. I did something similar for an AWS Lambda layer, here's what I was able to delete (this was for Pytorch 1.1, things may have changed since):

find . -type d -name "tests" -exec rm -rf {} +
find . -type d -name "__pycache__" -exec rm -rf {} +
rm -rf ./{caffe2,wheel,pkg_resources,boto*,aws*,pip,pipenv,setuptools} 
rm ./torch/lib/libtorch.so 
rm -rf ./torch/test


If that's not enough, you need to look into using ONNX or TorchScript/torch.jit. ONNX exports your model to a binary protobuf file. TorchScript exports it to a serialized form that can be run in a C++ environment.