Who would want to use an Apple-centric language for ML, seriously?
Apple hardware is outright incompatible to the kind of hardware we use daily in machine learning workstations.
Apple hardware is outright incompatible to the kind of hardware we use daily in machine learning workstations.
Based on how you wrote your comment, I'm guessing you may not know this S4TF is a Google initiative.
Yes, Chris Lattner used to work for Apple but he was at Google Brain during the start of this project. When his team wanted to create a language where automatic differentiation and gradient descent was a 1st-class concept in the core syntax (i.e. without libraries) of the programming language , they looked at Rust, Julia, Swift, etc[1]. They ended up choosing Swift as the base language to extend with new ML syntax.
Also, Chris has said in previous interviews that he thought of Swift as general purpose language and that hoped it would be used outside of Apple's ecosystem.
EDIT to address the confusion where some assume Apple hardware dictates the direction of S4TF: For those not aware, Google's hardware TPU (Tensor Processing Unit)[2] is built with custom ASIC chips and not ARM nor Apple Silicon chips. Presumably, S4TF would run natively against Google's TPU. In other words, the goals and execution targets of S4TF are not restricted by Apple's ecosystem of macOS/iOS/Macbooks/iMacs/iPads in any way. Yes, the SwiftUI framework is Apple-specific but Swift-the-core-language-syntax[3] is not.
[to downvoters: if I wrote inaccuracies, please correct me.]
[1] https://github.com/tensorflow/swift/blob/master/docs/WhySwif...
[2] https://en.wikipedia.org/wiki/Tensor_Processing_Unit
[3] https://docs.swift.org/swift-book/ReferenceManual/zzSummaryO...
And you just ignored the hardware comment
Seems kind of obvious.
Or Linux/Windows :(. Losing Nvidia/CUDA kind of killed ML on macOS overnight.
I think Kitura only existed as a long shot for kicking off their cloud offering. They were hoping iOS developers would code their backends in Swift/Kitura and host on IBMs cloud.
But that never happened - so Kitura was killed. Swift on the server is still niche. And the companies that used a Swift backend overwhelmingly went with https://vapor.codes instead. It is faster, and has a nicer API.
I’d also say that it appears that Swift is going to be a great language for machine learning. Things like calculating gradients are built in to the language and you can import Python to fill in gaps until Swift libraries are ready. And Swift is quite fast.