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Thanks, it is a well written article. The only part which tarnishes an otherwise excellent article is the language comparisons portion. Such things are best avo

Thanks, it is a well written article. The only part which tarnishes an otherwise excellent article is the language comparisons portion. Such things are best avoided as they almost always reflect the biases of or where the author has the most experience. As you acknowledge, you were unfair to Julia. Julia has excellent support for general purpose programming. It has support for multiple dispatch, a quite powerful organizational construct rarely found in other languages, for example. Where it might fall short is in the number of general purpose libraries but that shouldn’t matter as much for ML. It certainly isn’t lacking for numeric libraries. For Java, it’s unclear what you mean by JVM issues but with GraalVM and Scala or Kotlin, I fail to identify any major issues. F# on .net core can be distributed without installing a runtime nor does garbage collection limit its expressiveness. It is among the fastest, in the Go/Java/Ocaml/Swift tier, which is next fastest after C/C++/Rust/Fortran. Compare https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/fsharpcore-java.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/... to https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/swift.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/.... Setting aside the problems of microbenchmarks and unidiomatic code, they’re both within spitting distance of each other, both wining and losing with no significant margin. Their runtime also recently introduced SIMD and fine-grained memory management support. I'd say practically speaking, Swift's current great weakness is its cross platform support, when compared to Julia, Kotlin/Scala or C#/F#. Swift is an excellent language and it’s great a numeric programming ecosystem is being built for it. There’s no need to justify its existence by misrepresenting or downplaying the capabilities of competing ecosystems. I also saw this occur for why differentiable programming in Swift and not Julia. The more the merrier, I say. Let the various communities collaborate and share ideas in order to better explore the space of possibilities and ideas.

throwlaplace 7y ago
>F# on .net core can be distributed without installing a runtime how?
- Cybiote 7y ago
  By bundling. I believe there are couple a options to reduce the resulting package size some. As with all things, it's a matter of priorities when weighing language and deployment options.
ZeroCool2u 7y ago
On paper F# seems pretty great. Unfortunately, in practice it was the first and so far the only language I've ever used that I never found myself enjoying. I wrote a simple, but relatively robust toy language interpreter in it, so the time I put in wasn't trivial and I kept thinking it would get better, but no such luck. This is just my anecdotal experience though, I know some companies have been wildly successful with F# as the technical foundation for their entire platform. Jet is a great example of one such company.
- Cybiote 7y ago
  Certainly, not every language will fit everyone's preferred style of thinking and approach to breaking down problems. This is why I think a diversity in available options is so useful.
zelly 7y ago
> JVM issues stop the world GC (my car ran you over because of a random unpredictable pause!) its bytecode is object-oriented impossible at the moment to do CUDA style GPU programming without horrible JNI calls > F# on .net core can be distributed without installing a runtime nor does garbage collection limit its expressiveness F# has two big crippling factors: (1) the .NET at the end of its name and (2) functional programming, which is a huge barrier for 99% of developers (and it's not functional enough for the other 1%). F# is dead in the water--I mean that respectfully. To replace Python for deep learning, you need a language that other people will actually use, not should.
- Cybiote 7y ago
  > stop the world GC (my car ran you over because of a random unpredictable pause!) Given a certain baseline of available resources and for the places where GC issues can be problematic (regimes I do not believe common) unpredictability of GC is the main issue, which can be readily addressed. You can for example, allocate and then manually manage or opt for a specialized JVM. If startup-time is an issue there are native compilation options. Or just not use the JVM, that's an option too. I'll also note that Automatic reference counting is no panacea either and Python is no better suited for the scenario you've given. Personally, I've not found this to be an issue given modern low pause concurrent GC but your mileage may vary. > its bytecode is object-oriented > impossible at the moment to do CUDA style GPU programming without horrible JNI calls I don't think these are deal breakers. More easily writing kernels in a high level language is very much an open problem. Even Tensorflow faces this issue, with most workflows optimized for a handful of prewritten kernels. On the JVM there are options such as https://index.scala-lang.org/thoughtworksinc/compute.scala/benchmarks/0.4.3+122-322540ce?target=_2.12 https://index.scala-lang.org/thoughtworksinc/compute.scala/b... or http://aparapi.com/ http://aparapi.com/ for GPU backed ND-arrays or JVM translation. > F# has two big crippling factors: (1) the .NET at the end of its name and (2) functional programming, which is a huge barrier for 99% of developers (and it's not functional enough for the other 1%). F# is dead in the water--I mean that respectfully. To replace Python for deep learning, you need a language that other people will actually use, not should. I don't think (1) is true or even if so, see its technical relevance. If you're doing numeric/array-based differentiable programming then you shouldn't have any problem with functional programming. I'd even argue functional programming makes things easier as you get many things free from existing combinators. Many concepts are naturally expressed with the features such languages tend to have. I don't think Python needs replacement, I'd much more rather see interoperability and language agnosticism. I can tell you that for each of Julia, Haskell, Scala, Kotlin, Ocaml, Nim, F#, Rust and of course Swift, at least one fascinating machine learning library is being built. I think that's a great thing.
igouy 7y ago
> Setting aside the problems of microbenchmarks and unidiomatic code … We don't get to set aside problems when they would undermine a premise of your conclusion. Use of the benchmarks game as evidence must imply that you have confidence in that evidence.
- Cybiote 7y ago
  It does not. Because, since both systems in comparison are in violation, when normalized to just this setting, the comparisons are meaningful. That is, while microbenchmarks are not something that should replace task relevant testing, they do have utility as a coarse indicator. The information should be taken with a large helping of uncertainty but it also points in the general correct direction in terms of relative ordering of the compared. The reality is often that other things will dominate. Things such as computational complexity, appropriateness and optimizations of data structures in use, I/O bounds, cache locality and specific details of the problem that will tend to reduce and not magnify the differences between languages near each other in a relative ordering, when things are done properly. Or slow things majorly when things are not done properly. This holds especially if idiomatic code is not anymore expensive to write in any of the compared languages, as is the case here.
  - igouy 7y ago
    > both systems in comparison are in violation Invalid + Invalild != Valid > they do have utility as a coarse indicator Which, once again, must imply that you have confidence in that evidence.
    - Cybiote 7y ago
      Your arithmetic is not relevant to my point because as I said, when normalized to the same domain, it is valid. > Which, once again, must imply that you have confidence in that evidence. Nope, think of it as a bayesian update which induces a non-zero but low relative entropy, with respect to the new posterior distribution.
      - 7y ago

throwlaplace 7y ago

>F# on .net core can be distributed without installing a runtime how?

Cybiote 7y ago
By bundling. I believe there are couple a options to reduce the resulting package size some. As with all things, it's a matter of priorities when weighing language and deployment options.

ZeroCool2u 7y ago

On paper F# seems pretty great. Unfortunately, in practice it was the first and so far the only language I've ever used that I never found myself enjoying. I wrote a simple, but relatively robust toy language interpreter in it, so the time I put in wasn't trivial and I kept thinking it would get better, but no such luck. This is just my anecdotal experience though, I know some companies have been wildly successful with F# as the technical foundation for their entire platform. Jet is a great example of one such company.

Cybiote 7y ago
Certainly, not every language will fit everyone's preferred style of thinking and approach to breaking down problems. This is why I think a diversity in available options is so useful.

zelly 7y ago

> JVM issues stop the world GC (my car ran you over because of a random unpredictable pause!) its bytecode is object-oriented impossible at the moment to do CUDA style GPU programming without horrible JNI calls > F# on .net core can be distributed without installing a runtime nor does garbage collection limit its expressiveness F# has two big crippling factors: (1) the .NET at the end of its name and (2) functional programming, which is a huge barrier for 99% of developers (and it's not functional enough for the other 1%). F# is dead in the water--I mean that respectfully. To replace Python for deep learning, you need a language that other people will actually use, not should.

Cybiote 7y ago
> stop the world GC (my car ran you over because of a random unpredictable pause!) Given a certain baseline of available resources and for the places where GC issues can be problematic (regimes I do not believe common) unpredictability of GC is the main issue, which can be readily addressed. You can for example, allocate and then manually manage or opt for a specialized JVM. If startup-time is an issue there are native compilation options. Or just not use the JVM, that's an option too. I'll also note that Automatic reference counting is no panacea either and Python is no better suited for the scenario you've given. Personally, I've not found this to be an issue given modern low pause concurrent GC but your mileage may vary. > its bytecode is object-oriented > impossible at the moment to do CUDA style GPU programming without horrible JNI calls I don't think these are deal breakers. More easily writing kernels in a high level language is very much an open problem. Even Tensorflow faces this issue, with most workflows optimized for a handful of prewritten kernels. On the JVM there are options such as https://index.scala-lang.org/thoughtworksinc/compute.scala/benchmarks/0.4.3+122-322540ce?target=_2.12 https://index.scala-lang.org/thoughtworksinc/compute.scala/b... or http://aparapi.com/ http://aparapi.com/ for GPU backed ND-arrays or JVM translation. > F# has two big crippling factors: (1) the .NET at the end of its name and (2) functional programming, which is a huge barrier for 99% of developers (and it's not functional enough for the other 1%). F# is dead in the water--I mean that respectfully. To replace Python for deep learning, you need a language that other people will actually use, not should. I don't think (1) is true or even if so, see its technical relevance. If you're doing numeric/array-based differentiable programming then you shouldn't have any problem with functional programming. I'd even argue functional programming makes things easier as you get many things free from existing combinators. Many concepts are naturally expressed with the features such languages tend to have. I don't think Python needs replacement, I'd much more rather see interoperability and language agnosticism. I can tell you that for each of Julia, Haskell, Scala, Kotlin, Ocaml, Nim, F#, Rust and of course Swift, at least one fascinating machine learning library is being built. I think that's a great thing.

igouy 7y ago

> Setting aside the problems of microbenchmarks and unidiomatic code … We don't get to set aside problems when they would undermine a premise of your conclusion. Use of the benchmarks game as evidence must imply that you have confidence in that evidence.

Cybiote 7y ago
It does not. Because, since both systems in comparison are in violation, when normalized to just this setting, the comparisons are meaningful. That is, while microbenchmarks are not something that should replace task relevant testing, they do have utility as a coarse indicator. The information should be taken with a large helping of uncertainty but it also points in the general correct direction in terms of relative ordering of the compared. The reality is often that other things will dominate. Things such as computational complexity, appropriateness and optimizations of data structures in use, I/O bounds, cache locality and specific details of the problem that will tend to reduce and not magnify the differences between languages near each other in a relative ordering, when things are done properly. Or slow things majorly when things are not done properly. This holds especially if idiomatic code is not anymore expensive to write in any of the compared languages, as is the case here.
- igouy 7y ago
  > both systems in comparison are in violation Invalid + Invalild != Valid > they do have utility as a coarse indicator Which, once again, must imply that you have confidence in that evidence.
  - Cybiote 7y ago
    Your arithmetic is not relevant to my point because as I said, when normalized to the same domain, it is valid. > Which, once again, must imply that you have confidence in that evidence. Nope, think of it as a bayesian update which induces a non-zero but low relative entropy, with respect to the new posterior distribution.
    - 7y ago