[cgarciae]

One thing not mentioned in the original "Why Swift for Tensorflow" document and was a mayor source of conflict when the differentiable programming feature was formally proposed by the S4TF as a standard Swift feature: Swift has no mechanisms for metaprogramming. The reason is that Automatic Differentiation can be implemented 100% using metaprogramming, instead the S4TF team had to create internally certain features for this, that is probably one of the reasons why it took so long to get the most basic stuff working.

In retrospective you can really say Swift was a bad choice for the project because the time to market was much slower than it could be vs e.g choosing Julia. The other thing they didn't take into account was the actual market, that is, the Data Science ecosystem in Swift is non-existente, you have an excellent Deep Learning library standing alone without a numpy, a pandas, a scipy, a opencv, a pillow, ect, which makes doing real application with it nearly impossible.

That said, Swift as a language is amazing, doing parallel computation is so easy, not having a garbage collector makes it super efficient. Its the kind of thing we need, but the language right now is not in the right state.