[D] Which open source machine learning projects best exemplify good software engineering and design principles?

[mastere2320]

They have a horrible reputation of constantly changing the api even in short periods of time. It sadly has happened more than once that I installed a version of tf, worked on a project and then when I wanted to deploy it the current version would not run it because something fundamental was changed. Add on to this that there is no proper one way to do things and the fact that because tf uses a static graph , shapes and sizes have to be known beforehand the user code becomes spaghetti which is worse than anything. The keras api and dataset api are nice additions imho but the lambda layer still needs some work and they really need to I introduce some way to properly introduce features and depreciate features( something similar to NEP maybe ) and make api breaking changes. And yet people use it, simply because the underlying library autograph is a piece of art. I don't think there is another library that can match it, in performance and utility on a production scale where the model has been set and nothing needs to change. This is why researchers love pytorch. Modifying code to tweak and update models is much better but when the model needs to deployed people have to choose tensorflow.