Swift for TensorFlow – A system for deep learning and differentiable computing
1–10 of 142 posts
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#2I think the title is cut off at the end.
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#3Who 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.
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#4Who 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.
People that make apps for Apple hardware?
Seems kind of obvious.
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#5Who 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.
The appeal isn't broad enough
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#6Who 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.
[deleted]
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#7Who 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.
Swift is gaining momentum outside the Apple ecosystem. Web servers are being built and the Tensorflow team explains in length it’s advantages for developing ML models with it.
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#8Who 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.
I find swift really good in terms of ease of coding and speed. But yeah, I would have preferred Julia or something than swift.
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#9Who 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.
>Who would want to use an Apple-centric language for ML, seriously? Apple hardware is outright incompatible
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...
Re: Swift for TensorFlow – A system for deep learning and differentiable computing
#10Who 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.
Working on expanding it outside of the Apple/iOS ecosystem but many (probably most) developers use Mac. You can write Swift code in Linux right now.
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.