5 ms·

Why can't Julia fill that role?

by tanlermin 11y ago

andybak 11y ago
Which role? A general purpose programming language with an emphasis on readability?
- tanlermin 11y ago
  Yes...but one that is also very fast, portable and with great generic programming.
  - 21 11y ago
    Stuff like Numba greatly decreases the need for Julia. C++17 also feels surprisingly dynamic, and together with Cython for easy Python-C++ interop also decreases the need for Julia. And reports from the Julia world are not exactly encouraging - http://danluu.com/julialang/ http://danluu.com/julialang/
    - aut_dan 11y ago
      That report is over a year old, which for a 4 year old language is a very long time. The top comment in [1] is from a week ago and highlights why it is now mostly invalid. [1] https://news.ycombinator.com/item?id=11070764 https://news.ycombinator.com/item?id=11070764
  - andybak 11y ago
    I had never considered Julia as a general purpose language. I assumed it was targetted mainly at data science etc.
    - ced 11y ago
      The core language is general purpose, and IMO really good for high-performance work, but the community is focused on numerical code, so there aren't a lot of libraries for non-numerical/scientific/financial work at this point in time.
deleted 11y ago
[deleted]
jordigh 11y ago
Because it's young and we still don't know how it will pan out.
opensandwich 11y ago
Try to do machine learning in Julia; it is difficult. If I want to say fit a gradient boosted tree or SVM its not support in Julia, whilst appears in Python/R libraries. Also, with Spark being more and more popular in the data science landscape, the lack of Julia bindings is also a no for the data scientists I work with (Spark has Python/R bindings).
- aut_dan 11y ago
  You will have to reach a little deeper for machine learning methods not supported by Julia. XGBoost has a Julia interface and you can google Julia SVM for myriad of alternatives. Packages like Mocha and MXNet are a few deep learning alternatives. PyCall is also an easy solution for interfacing with Python for things such as pyspark. It also has some of the most convenient to use parallel / distributed computing tools for numerical computing. Point being that even if Julia is not there to replace Python, there is still a strong case for using it as a way to augment Python workflow.
  - eva1984 11y ago
    If Julia just want to replace Python as the glue interface, it seems to have no chance winning...What it can do, as a glue layer, that Python cannot do?
    - ced 11y ago
      It's not meant to compete with Python as a glue language. The point is that you can start using Julia right now and be productive by calling other languages' libraries to fill in the holes.
      - TheLogothete 11y ago
        The questions is why would I do that? Because Julia is new?
        ced 11y ago

Why can't Julia fill that role?

andybak 11y ago

Which role? A general purpose programming language with an emphasis on readability?

tanlermin 11y ago
Yes...but one that is also very fast, portable and with great generic programming.
- 21 11y ago
  Stuff like Numba greatly decreases the need for Julia. C++17 also feels surprisingly dynamic, and together with Cython for easy Python-C++ interop also decreases the need for Julia. And reports from the Julia world are not exactly encouraging - http://danluu.com/julialang/ http://danluu.com/julialang/
  - aut_dan 11y ago
    That report is over a year old, which for a 4 year old language is a very long time. The top comment in [1] is from a week ago and highlights why it is now mostly invalid. [1] https://news.ycombinator.com/item?id=11070764 https://news.ycombinator.com/item?id=11070764
- andybak 11y ago
  I had never considered Julia as a general purpose language. I assumed it was targetted mainly at data science etc.
  - ced 11y ago
    The core language is general purpose, and IMO really good for high-performance work, but the community is focused on numerical code, so there aren't a lot of libraries for non-numerical/scientific/financial work at this point in time.

deleted 11y ago

[deleted]

jordigh 11y ago

Because it's young and we still don't know how it will pan out.

opensandwich 11y ago

Try to do machine learning in Julia; it is difficult. If I want to say fit a gradient boosted tree or SVM its not support in Julia, whilst appears in Python/R libraries. Also, with Spark being more and more popular in the data science landscape, the lack of Julia bindings is also a no for the data scientists I work with (Spark has Python/R bindings).

aut_dan 11y ago
You will have to reach a little deeper for machine learning methods not supported by Julia. XGBoost has a Julia interface and you can google Julia SVM for myriad of alternatives. Packages like Mocha and MXNet are a few deep learning alternatives. PyCall is also an easy solution for interfacing with Python for things such as pyspark. It also has some of the most convenient to use parallel / distributed computing tools for numerical computing. Point being that even if Julia is not there to replace Python, there is still a strong case for using it as a way to augment Python workflow.
- eva1984 11y ago
  If Julia just want to replace Python as the glue interface, it seems to have no chance winning...What it can do, as a glue layer, that Python cannot do?
  - ced 11y ago
    It's not meant to compete with Python as a glue language. The point is that you can start using Julia right now and be productive by calling other languages' libraries to fill in the holes.
    - TheLogothete 11y ago
      The questions is why would I do that? Because Julia is new?
      - ced 11y ago