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>And we havent even touched on the elephant in the Room - Swift may soon hook into OSX Metal

Not sure whether I understood your statement correctly, but you can use Swift for using metal API already. Swift is not the bottleneck for TensorFlow, PyTorch like open-source machine learning library appearing for macOS based on metal, it is the closed nature of mac ecosystem itself.

Look at the flak Google, Facebook get regarding these projects in-spite of them being open-source. If an equivalent metal (only) based open-source machine learning of this scale did pop up with Apple's help; it may not go well with the ML/DL community as sharing & collaboration is valued high here. Apple has even started to publishing open ML papers to attract talent.

Then again, Apple doesn't get as much scrutiny as Google/Facebook does with the tech community, take a look at any HN thread on GoLang/Swift; the discussion on the former goes to the company rather than the programming language as in the case of latter in-spite of both being open-source.

I'm aware that you can hook into metal using swift already. I meant the shader language (which is a variant of c++), though there are issues and proposals to make it swift centric.

When first announced, I was very upbeat on S4TF and the XCode and Swift drops with TF and some examples more or less worked and were interesting.

Since the first early release, which largely worked for me, subsequent releases have generally not worked for me. I even had much more trouble with S4TF than, for example, the Haskell TF bindings that always take a bit of setup since I don’t use the provided docker setup.

A little off topic, but, the Julia Flux ML library is worth looking at if you wanna “turtles all the way down” setup.

While Swift may be the "latest and greatest" language with official support, if you look at https://www.tensorflow.org/api_docs, it's only one of several (the others being C++, Java, JS, Go and of course Python). Plus, there are community-supported bindings for C#, Haskell, Julia, Ruby, Rust and Scala). Just to make the picture complete...

Swift for tensor flow aren't bindings - it's a ground up rearchitecting of tensorflow

For Mac OS only apparently.

not true

https://github.com/tensorflow/swift/blob/master/Installation...

Having a package available for a specific OS doesn't mean it is usable, specially when it still requires conditional compilation to import either Darwin or Glibc, instead of Foundation.

And I don't see any Windows there.

Then there is the whole set of libraries that only compile on Apple platforms, no IDE, no Playgrounds and so on.

>specially when it still requires conditional compilation to import either Darwin or Glibc, instead of Foundation.

you don't need to compile from source - binaries are distributed (if you actually read my link instead of assuming it was just github source you would see that).

>And I don't see any Windows there.

yes they're aware of this and it's being worked on

>Then there is the whole set of libraries that only compile on Apple platforms, no IDE, no Playgrounds and so on.

you're moving goal posts in order to be able to have an axe to grind.

your comment was

>For Mac OS only apparently.

the fact that there's a runtime that runs on linux shows that that was incorrect.

>set of libraries that only compile on Apple platforms

i don't know what you're talking about here - s4tf libraries compile on all platforms e.g. https://github.com/eaplatanios/swift-rl

>no IDE, no Playgrounds and so on.

i don't see how xcode being developed by apple (not the s4tf team) has anything to do with whether s4tf is multiplatform.

Not moving goalposts at all.

Swift is only usable in Apple OSes.

Making a tiny subject of its ecosystem available outside of Apple platform doesn't make it usable for something that pretends to replace Python ecosystem on data science.

> it still requires conditional compilation to import either Darwin or Glibc, instead of Foundation

Well, swift-corelibs-foundation has made a lot of progress, and strictly speaking, to a certain point you don't actually want to hide some of the implementation details from the developer. This isn't Java - natively calling out to C is a thing that Swift can and will do.

> I don't see any Windows there.

Look harder. Swift for Windows: https://github.com/compnerd/windows-swift

> no Playgrounds

Swift Playgrounds: https://github.com/save-buffer/swift-repl

> no IDE

official VSCode work: https://github.com/apple/sourcekit-lsp/ + https://github.com/apple/sourcekit-lsp/tree/master/Editors/v...

> and so on

Do tell. What other concerns do you have? You seem deeply misinformed. While Swift may not yet have an ecosystem outside of Apple, it is factually incorrect to say that it is not usable outside of it.

Microsoft also provides Tensorflow bindinds via ML.NET.

So any language that can target .NET is also able to plug into it, and I certainly would pick F# over Swift.

> Microsoft also provides Tensorflow bindinds via ML.NET.

Well Swift for Tensorflow is more than just a binding to begin with. The difference in maintenance is that Google officially supports Swift whereas Microsoft would be always catching up to keep theirs updated.

> So any language that can target .NET is also able to plug into it, and I certainly would pick F# over Swift.

I can use both Swift for my iOS app (inferencing), training is also possible thanks to Python interoperability (@dynamicCallable), soon to be replaced by native C++ interoperability.

If I chose F#, as much as you may think it is a good choice unless your stack/team is using Microsoft technologies, I see this as a potential sunken cost here and I certainly wouldn't use it for this case and would rather rely on the active maintenance of the Swift/Tensorflow team at Google to improve this rather than using another great language for an unsupported use case.

The big difference being that I can use ML.NET on macOS, Linux, Windows, with Tensorflow lite on the roadmap. Whereas Swift, pretty much is Apple only.

Even your examples are a proof of that.

Zero persons from Swift/Tensorflow team at Google are working on improving Linux/Windows support.

Linux was been mostly volunteer and some IBM contributions.

Windows are just volunteers, and have restarted the port multiple times.

And R has Tensorflow bindings too. https://tensorflow.rstudio.com/

I don’t know why you are painting this as Swift vs Rust when they are apples to oranges. Why so defensive? They’re drastically different languages operating at different abstraction levels with different (though overlapping) use cases, with different motivations and design goals.

I had the feeling Swift was much higher level than Rust and it also didn't bring much new to the table.

I think the OP is making that point that Python was successful because of how relatively simple and high-level it is, thus Swift has potential in that area for the same reason. Rust is hardly approachable by "the masses", certainly all the ML-first engineers who are not as hard-core on the programming mindshare, hence why Python became king.

Everything about TF on Swift says zombie project.

I'll be amazed if it ever gets any significant foothold in the ML world.

This is really new to me, I always thought of Swift as the iOS thing to look into when doing something there.

Very interesting that Google is pushing Swift for their Tensorflow Platform. Any ideas why Swift is of interest for Google?

They hired one of the Swift leaders for the tensor flow team

Swift playgrounds seem very much aligned with notebook style ML development. They're in many ways a much more powerful version of notebooks.

https://www.apple.com/swift/playgrounds/

You can read why Tensorflow team chose Swift here[1].

[1]https://github.com/tensorflow/swift/blob/master/docs/WhySwif...

I whish it was kotlin instead of Swift. But as much as I want to move away from python, there are reasons to stick with it (ML libraries and low entry barrier for non programers)

Swift for writing Metal shaders? That's the first I've heard of this, and honestly I find that doubtful but what do I know.

>And all of them will soon be backed using Tensorflow Swift

Lol at this. It's ~5 years before S4TF has feature parity with TF let alone pytorch. I watch their design meetings almost every Friday (they're public) and not only is the core autodiff runtime nowhere near 1.0 (just a couple of weeks ago they were talking about replacing their bespoke system with XLA) there's almost no differentiable function type library (nn.functional). Not to mention ecosystem (there are probably, at most, 20 people, including the S4TF team, consistently using S4TF). This is not to say that they're not doing great work but that's it's far off.

Edit: I forgot to also say they're going through the Swift evolution process (since they're building the autodiff into the Swift compiler) and it's completely unknown whether it'll be accepted. I have no idea what will happen to the project if it doesn't but if they end up having to maintain their own Swift runtime then S4TF is dead in the water because no one will be comfortable deploying two runtimes and even then no one will trust Google to maintain.

How can I audit the Friday design meeting?

https://docs.google.com/document/d/1Fm56p5rV1t2Euh6WLtBFKGqI...

As one of those 20 people, I feel like I'm in good company!

Five years is pessimistic. The core library is still in a state of flux, but the team is getting the fundamentals hammered out. Once the foundation is in place, adding higher level apis should be relatively straightforward.

Re: XLA/MLIR, s4tf is actively trying to coordinate with other groups within google. Whatever short term losses there are in time/energy will be paid off down the road when the project can piggyback off other people's progress.

Finally, while the swift evolution process isn't going as quickly as would be desired, the changes needed to merge the tensorflow branch have been identified and are being worked on. It's just a matter of time till the two projects are in sync.

Thank you for your work. Although I still wish you guys had done Kotlin instead of Swift.

I haven't seen people love a language without evangelism like Kotlin. And you already had the world's biggest ecosystem (because of Android).

And Kotlin native is pretty good. i daresay you would have had better control of the compiler there.

> The amount of evangelism money that Google is spending

Same for Rust, Swift, Go... it's like we are in another bubble like the dot-com and everything is hype driven.

Well... not everything, Rust is actually good. Just sit back enjoy the effects of the investment in this stuff, and don't worry about other people being foolish with their money.

Even Go is not that bad tbh. It's basically Java/.NET done right, and that makes it quite appropriate for some uses.

I still have no idea how to get/run/make/compile/deploy Go stuff. Sure, there's some GOPATH, some packages, dep? go get?

I have no idea how, but 1-2 years ago when Kubernetes was new I tried it, and somehow got it to build (obviously I must have been using the stuff from the README) - but I haven't learned it.

Whereas with Rust? It's cargo. Done. That's up. Or rustup if you don't have anything yet. With Java? It's IntelliJ or the OpenJDK.

`go run` doesn't seem too hard to me.

https://golang.org/cmd/go/#hdr-Compile_and_run_Go_program

I haven't encountered it so far.

Last time I fiddled with something Go based was wit cfssl (cloudflare's excellent PKI thingie), and that uses something else. I don't remember what, it's in the GitHub Action YML file now. (Which alas turned out to be a big hot air pipeline :// )

I plan on learning a bit more about Go, but last time with the 2.0 proposal and the go mod/dep thing it seemed like there will be some streamlining, so I decided to postpone the immersion into the Go-cean.

Yeah, every time I have tried to get a Go project set up or built from source I end up throwing in the towel 15 minutes later in frustration. Never had that kind of experience with any other language

Yeah, vendoring in Go can be a bit frustrating. One of the great benefits of the JVM is how dependencies are isolated to the project so nicely, bundled into the jar, and everything "just works" no matter where you take that jar. That's one of the most liberating aspects of working with Java/Kotlin/Clojure, etc.

Not it is not.

The only thing that is has done right versus Java is having AOT compilation since the begining (.NET always supported it via NGEN).

Not to mention that Java and .NET are platforms for programming languages, and already there Go loses big time versus what we have available on our toolbox.

.NET done right? haha really? I find Go to be an order of magnitude less productive than C# and more complicated to get up and running with actually building useful things. Go over-complicates things which C# and .NET make easy while also having less readable code and less robust tooling.

Although I am not on the Rust team and do not speak for them, I have enough familiarity Rust's funding situation to assure you that there is no budget available for developer evangelism. :P

It's not obvious to everyone yet but the modern language that will become the most mainstream is obviously Kotlin. It has almost all modern language features, a ton of justified syntax sugars, and is 100% compatible with the Java ecosystem making it the most production ready among "modern languages"

The only other language with such a property is to my knowledge, typescript.