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IMHO, no, stick with Python. Of course you can fiddle with Julia but Python is used in many fronts and much more versatile. If Python were a blocker for the ad

IMHO, no, stick with Python. Of course you can fiddle with Julia but Python is used in many fronts and much more versatile. If Python were a blocker for the adoption and development of ML, it wouldn't be the default language of the most popular ML libs. The reason is, the high performant code is written in C++/C/Fortran. Python is just used to glue everything and provide a nice and rich interface. That's what really matters. To me Julia is more to Fortran than Python. And it doesn't have many usages outside numeric programming. Scapping the Web, building rest APIs, Performing Data Analysis, Automation scripts is much easier in Python than Julia or Swift. Edit: typos

dunefox 6y ago
> much more versatile. > To me Julia is more to Fortran than Python. And it doesn't have many usages outside numeric programming. Again, you're making unsubstantiated claims. > Python is just used to glue everything and provide a nice and rich interface. That's what really matters. So, now I have to not only learn Python but also Fortran/C++/C because the underlying library that I might want to adapt is written in one of these languages. In Julia the DL library, for example, is written in Julia. What you are claiming is a pro is actually a con. > Scapping the Web, building rest APIs, Performing Data Analysis, Automation scripts is much easier in Python than Julia or Swift. That might be true for Swift, but certainly not for Julia.
- nl 6y ago
  So, now I have to not only learn Python but also Fortran/C++/C because the underlying library that I might want to adapt is written in one of these languages. Basically the only time you'll want to do this from Python is if there is specific Fortran or C++ library you want to use. You'd have to do the same in Swift or Julia in this case.
  - dunefox 6y ago
    Only if the library is not written in Julia, yes. The point here is that Julia is efficient enough that deep learning libraries can be written in pure Julia, not C/C++/Fortran.
    - nl 6y ago
      And yet... no widely used DL library is in Julia, and they are all in Python. It's kind of a silly point to try to score: "it's possible to write an efficient deep learning library in Julia (although no one has done it yet), and yes, you can do the same in Numpy in Python, and XLA in Python will outpeform it, but someone else wrote some C/C++ there to make that possible!" You are much more likely to want to write CUDA kernels (in C!) than you are to write C framework code to interface with Python for machine learning. The person is looking to "get into ML". I've been working as a professional ML developer for 6 years, and I've never written any C or C++ for it.
      - BadInformatics 6y ago
        > although no one has done it yet This may be generally true (though the benchmarks I've seen show Knet.jl and sometimes Flux.jl on par with TF/PyTorch with a single machine + single GPU), but there are definitely domains where it is categorically not. The most prominent one is neural *DEs, where the SciML [1] ecosystem has SOTA performance. You can really see Python/C++-based frameworks struggle here because they have slow "glue code" and don't (one could argue can't effectively) optimize for latency. That's not a problem for most CV models and transformers, but really stunts research into more dynamic approaches. [1] https://sciml.ai/ https://sciml.ai/
        nl 6y ago
        This sounds more like it's a new field. I started looking for benchmarks (because it sounds like the kind of thing JAX would do well) and the very first link I clicked included: Wraps for common C/Fortran methods like Sundials and Hairer's radau which is exactly what was claimed wasn't needed.
        amkkma 6y ago
        Those aren't needed, but they are options. "he templated implementation allows arbitrary array and number types to be compatible, giving compatibility with arbitrary precision floating point numbers, GPU-based computations, unit-checked arithmetic, and other features. " Those are exactly the things wrapping fortran and c won't give you
- miguendes 6y ago
  > So, now I have to not only learn Python but also Fortran/C++/C because the underlying library that I might want to adapt is written in one of these languages. In Julia the DL library, for example, is written in Julia. What you are claiming is a pro is actually a con. You don't need to learn C/C++ or fortran. I've been working with python and ML for about 4 years and haven't touched any C/C++ or fortran to get work done. I agree that it'd be better if we could do everything with just one language but the truth is that Julia is not that language. It's great for HPC but expressive enough for generic things like Web.
  - dklend122 6y ago
    How is it not generic enough for the web? See https://genieframework.com/ https://genieframework.com/ and interact.jl Once it can compile it web assembly (work in progress) it be the obvious choice.
mark_l_watson 6y ago
Two years ago when I started experimenting with Julia and the Flux DL library, I paused to code up non numerical problems in Julia: web scraping, making REST calls, SPARQL queries, build simple web site, etc. Julia was OK for all of these tasks. I am going to watch Julia adoption, and use it more, when and if it becomes more popular.
- dklend122 6y ago
  And It's improved by miles for those things

dunefox 6y ago

> much more versatile. > To me Julia is more to Fortran than Python. And it doesn't have many usages outside numeric programming. Again, you're making unsubstantiated claims. > Python is just used to glue everything and provide a nice and rich interface. That's what really matters. So, now I have to not only learn Python but also Fortran/C++/C because the underlying library that I might want to adapt is written in one of these languages. In Julia the DL library, for example, is written in Julia. What you are claiming is a pro is actually a con. > Scapping the Web, building rest APIs, Performing Data Analysis, Automation scripts is much easier in Python than Julia or Swift. That might be true for Swift, but certainly not for Julia.

nl 6y ago
So, now I have to not only learn Python but also Fortran/C++/C because the underlying library that I might want to adapt is written in one of these languages. Basically the only time you'll want to do this from Python is if there is specific Fortran or C++ library you want to use. You'd have to do the same in Swift or Julia in this case.
- dunefox 6y ago
  Only if the library is not written in Julia, yes. The point here is that Julia is efficient enough that deep learning libraries can be written in pure Julia, not C/C++/Fortran.
  - nl 6y ago
    And yet... no widely used DL library is in Julia, and they are all in Python. It's kind of a silly point to try to score: "it's possible to write an efficient deep learning library in Julia (although no one has done it yet), and yes, you can do the same in Numpy in Python, and XLA in Python will outpeform it, but someone else wrote some C/C++ there to make that possible!" You are much more likely to want to write CUDA kernels (in C!) than you are to write C framework code to interface with Python for machine learning. The person is looking to "get into ML". I've been working as a professional ML developer for 6 years, and I've never written any C or C++ for it.
    - BadInformatics 6y ago
      > although no one has done it yet This may be generally true (though the benchmarks I've seen show Knet.jl and sometimes Flux.jl on par with TF/PyTorch with a single machine + single GPU), but there are definitely domains where it is categorically not. The most prominent one is neural *DEs, where the SciML [1] ecosystem has SOTA performance. You can really see Python/C++-based frameworks struggle here because they have slow "glue code" and don't (one could argue can't effectively) optimize for latency. That's not a problem for most CV models and transformers, but really stunts research into more dynamic approaches. [1] https://sciml.ai/ https://sciml.ai/
      - nl 6y ago
        This sounds more like it's a new field. I started looking for benchmarks (because it sounds like the kind of thing JAX would do well) and the very first link I clicked included: Wraps for common C/Fortran methods like Sundials and Hairer's radau which is exactly what was claimed wasn't needed.
        amkkma 6y ago
        Those aren't needed, but they are options. "he templated implementation allows arbitrary array and number types to be compatible, giving compatibility with arbitrary precision floating point numbers, GPU-based computations, unit-checked arithmetic, and other features. " Those are exactly the things wrapping fortran and c won't give you
miguendes 6y ago
> So, now I have to not only learn Python but also Fortran/C++/C because the underlying library that I might want to adapt is written in one of these languages. In Julia the DL library, for example, is written in Julia. What you are claiming is a pro is actually a con. You don't need to learn C/C++ or fortran. I've been working with python and ML for about 4 years and haven't touched any C/C++ or fortran to get work done. I agree that it'd be better if we could do everything with just one language but the truth is that Julia is not that language. It's great for HPC but expressive enough for generic things like Web.
- dklend122 6y ago
  How is it not generic enough for the web? See https://genieframework.com/ https://genieframework.com/ and interact.jl Once it can compile it web assembly (work in progress) it be the obvious choice.

mark_l_watson 6y ago

Two years ago when I started experimenting with Julia and the Flux DL library, I paused to code up non numerical problems in Julia: web scraping, making REST calls, SPARQL queries, build simple web site, etc. Julia was OK for all of these tasks. I am going to watch Julia adoption, and use it more, when and if it becomes more popular.

dklend122 6y ago
And It's improved by miles for those things