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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 lear

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.

maxerickson 6y ago
People that make apps for Apple hardware? Seems kind of obvious.
xiaodai 6y ago
The appeal isn't broad enough
deleted 6y ago
[deleted]
iddan 6y ago
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.
- pjmlp 6y ago
  What momentum? Even IBM gave up on it.
  - rvz 6y ago
    Amazon? [0][1][2] [0] https://swift.org/blog/aws-lambda-runtime/ https://swift.org/blog/aws-lambda-runtime/ [1] https://aws.amazon.com/blogs/opensource/continuous-delivery-with-server-side-swift-on-amazon-linux-2/ https://aws.amazon.com/blogs/opensource/continuous-delivery-... [2] https://github.com/amzn/smoke-aws https://github.com/amzn/smoke-aws
    - pjmlp 6y ago
      Numbers?
  - rudedogg 6y ago
    I don't have any inside knowledge, but as a developer using Swift (that followed the Swift web frameworks closely): 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 https://vapor.codes instead. It is faster, and has a nicer API.
YetAnotherNick 6y ago
I find swift really good in terms of ease of coding and speed. But yeah, I would have preferred Julia or something than swift.
jasode 6y ago
>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/WhySwiftForTensorFlow.md https://github.com/tensorflow/swift/blob/master/docs/WhySwif... [2] https://en.wikipedia.org/wiki/Tensor_Processing_Unit https://en.wikipedia.org/wiki/Tensor_Processing_Unit [3] https://docs.swift.org/swift-book/ReferenceManual/zzSummaryOfTheGrammar.html https://docs.swift.org/swift-book/ReferenceManual/zzSummaryO...
- nsonha 6y ago
  For answering a rhetorical question? Yes of course that guy who came up with this also roots for swift to gain more traction outside Apple ecosystem but the reality hasn't going in that direction so far. There are plenty of cool languages out there like f#, julia, mathematica etc and the ML mob settled for python which is an average language so there is no reason to believe they are attracted to swift. And you just ignored the hardware comment
- dunefox 6y ago
  What a coincidence that someone would choose his own programming language in an evaluation. There was no good reason not to choose Julia.
ericmay 6y ago
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.
- pjmlp 6y ago
  Try to use Framework code on Linux.
- MiroF 6y ago
  I tried to use Swift recently while avoiding Xcode crud. It's a nightmare, definitely not going to be adopted until Apple goes more hands off.
vimy 6y ago
That might change with Apple Silicon and Apple’s new ML Compute framework.
- rudedogg 6y ago
  It seems like they'll be making more consumer focused GPUs/ML chips to me. I think Apple Silicon is exciting, but I imagine for serious work you'll still need an external enclosure and a dedicated GPU from AMD. Or Linux/Windows :(. Losing Nvidia/CUDA kind of killed ML on macOS overnight.
yunohn 6y ago
I agree on the hardware part. MacOS only supports AMD GPUs which are unsupported by Tensor flow...
- c16a 6y ago
  Tensorflow can work with ROCM which is equivalent to Nvidia's CUDA.
  - yunohn 6y ago
    There is no official support, only AMD's custom TF branch. Further ROCM itself works only on Linux. I believe most people writing Swift are using MacOS.
    - amkkma 6y ago
      And Julia can do custom ROCM codegen, either by itself or through array and kernel abstractions :) https://github.com/JuliaGPU/AMDGPU.jl https://github.com/JuliaGPU/AMDGPU.jl https://juliagpu.gitlab.io/KernelAbstractions.jl/ https://juliagpu.gitlab.io/KernelAbstractions.jl/ https://github.com/JuliaGPU/GPUArrays.jl/pulse https://github.com/JuliaGPU/GPUArrays.jl/pulse
      - yunohn 6y ago
        Indeed, the Julia scene looks enticing right now. If only AMD ROCM supported MacOS... :/

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.

maxerickson 6y ago

People that make apps for Apple hardware? Seems kind of obvious.

xiaodai 6y ago

The appeal isn't broad enough

deleted 6y ago

[deleted]

iddan 6y ago

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.

pjmlp 6y ago
What momentum? Even IBM gave up on it.
- rvz 6y ago
  Amazon? [0][1][2] [0] https://swift.org/blog/aws-lambda-runtime/ https://swift.org/blog/aws-lambda-runtime/ [1] https://aws.amazon.com/blogs/opensource/continuous-delivery-with-server-side-swift-on-amazon-linux-2/ https://aws.amazon.com/blogs/opensource/continuous-delivery-... [2] https://github.com/amzn/smoke-aws https://github.com/amzn/smoke-aws
  - pjmlp 6y ago
    Numbers?
- rudedogg 6y ago
  I don't have any inside knowledge, but as a developer using Swift (that followed the Swift web frameworks closely): 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 https://vapor.codes instead. It is faster, and has a nicer API.

YetAnotherNick 6y ago

I find swift really good in terms of ease of coding and speed. But yeah, I would have preferred Julia or something than swift.

jasode 6y ago

>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/WhySwiftForTensorFlow.md https://github.com/tensorflow/swift/blob/master/docs/WhySwif... [2] https://en.wikipedia.org/wiki/Tensor_Processing_Unit https://en.wikipedia.org/wiki/Tensor_Processing_Unit [3] https://docs.swift.org/swift-book/ReferenceManual/zzSummaryOfTheGrammar.html https://docs.swift.org/swift-book/ReferenceManual/zzSummaryO...

nsonha 6y ago
For answering a rhetorical question? Yes of course that guy who came up with this also roots for swift to gain more traction outside Apple ecosystem but the reality hasn't going in that direction so far. There are plenty of cool languages out there like f#, julia, mathematica etc and the ML mob settled for python which is an average language so there is no reason to believe they are attracted to swift. And you just ignored the hardware comment
dunefox 6y ago
What a coincidence that someone would choose his own programming language in an evaluation. There was no good reason not to choose Julia.

ericmay 6y ago

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.

pjmlp 6y ago
Try to use Framework code on Linux.
MiroF 6y ago
I tried to use Swift recently while avoiding Xcode crud. It's a nightmare, definitely not going to be adopted until Apple goes more hands off.

vimy 6y ago

That might change with Apple Silicon and Apple’s new ML Compute framework.

rudedogg 6y ago
It seems like they'll be making more consumer focused GPUs/ML chips to me. I think Apple Silicon is exciting, but I imagine for serious work you'll still need an external enclosure and a dedicated GPU from AMD. Or Linux/Windows :(. Losing Nvidia/CUDA kind of killed ML on macOS overnight.

yunohn 6y ago

I agree on the hardware part. MacOS only supports AMD GPUs which are unsupported by Tensor flow...

c16a 6y ago
Tensorflow can work with ROCM which is equivalent to Nvidia's CUDA.
- yunohn 6y ago
  There is no official support, only AMD's custom TF branch. Further ROCM itself works only on Linux. I believe most people writing Swift are using MacOS.
  - amkkma 6y ago
    And Julia can do custom ROCM codegen, either by itself or through array and kernel abstractions :) https://github.com/JuliaGPU/AMDGPU.jl https://github.com/JuliaGPU/AMDGPU.jl https://juliagpu.gitlab.io/KernelAbstractions.jl/ https://juliagpu.gitlab.io/KernelAbstractions.jl/ https://github.com/JuliaGPU/GPUArrays.jl/pulse https://github.com/JuliaGPU/GPUArrays.jl/pulse
    - yunohn 6y ago
      Indeed, the Julia scene looks enticing right now. If only AMD ROCM supported MacOS... :/