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I'm still not sold on Julia. Compared to R/python, the supposed benefit doesn't outweigh the cost of implementation and learning (my perception). I do a lot of

I'm still not sold on Julia. Compared to R/python, the supposed benefit doesn't outweigh the cost of implementation and learning (my perception). I do a lot of "big data" stuff in R/python, and I just use libaries written in C or C++.

ska 3y ago
This works fine as long as you only do things for which someone else has written the c/c++ parts you need already. Really the premise of Julia is to make their life easier, while leaving yours equivalently easy (or better).
- havercosine 3y ago
  This is spot on. I love Julia, whatever little I have dabbled into it. History of deep learning would have been slightly different if Julia had taken off as the implementation language. Might have encouraged more hackability and understandability. Wrapping our heads around Pytorch source or Jax source is tough. But this introspection gave me a reality check. In my own work, as much as I love Julia, I fallback to quickly using Python libs. In industry, most of the people's focus is on using someone's already written code. The byproduct is inheriting Python and C/C++ interface mess around dependency management. People needing to write their own algorithms are majority academics and researchers where Julia has flourished. The incentives of corporate work are setup to build on top of Python's ecosystem which means (sadly) Julia will stay in its niche.
hsjqllzlfkf 3y ago
"Just use C++ libraries" is good enough for "simple" operations on "simple" types that have been implemented in C++. If you want C-like performance for generic types without relying on someone having already implemented that in C++, use Julia.
EliasLittle 3y ago
I think you’re missing one of the key points of Julia, that others have also pointed out: you’re reliant on libraries other write in c/c++. Julia’s proposition is that the entire community should be able to read and contribute to these libraries. Unlike the python community where the vast majority of users could not read the source behind those libraries let alone contribute.
- Capricorn2481 3y ago
  Contributing to libraries may not be a goal for most people
  - havercosine 3y ago
    I agree with the observation (most people do not contribute to libraries), but I think the arrow of causality is little different. Python is used as a glue code in scientific computing on top of libraries written in C/C++. The development ergonomics of these languages are intimidating for many people, with both languages having enough footguns. The net result is no-one wants to peek under the hood and see how things are working. A fresher take on scientific computing like Julia, if it is mainstream, might enable more contributions and in general understandability of the black box algorithms.
    - Capricorn2481 3y ago
      Well put
- peterdsharpe 3y ago
  Generally this doesn't feel like a problem with Python, due to its massive user base advantage. Meta, Google, Microsoft, and many others contribute tens of billions of dollars annually to Python-ecosystem projects (PyTorch, Tensorflow, faster-cpython). Unless that funding differential changes I can't see Julia competing.

ska 3y ago

This works fine as long as you only do things for which someone else has written the c/c++ parts you need already. Really the premise of Julia is to make their life easier, while leaving yours equivalently easy (or better).

havercosine 3y ago
This is spot on. I love Julia, whatever little I have dabbled into it. History of deep learning would have been slightly different if Julia had taken off as the implementation language. Might have encouraged more hackability and understandability. Wrapping our heads around Pytorch source or Jax source is tough. But this introspection gave me a reality check. In my own work, as much as I love Julia, I fallback to quickly using Python libs. In industry, most of the people's focus is on using someone's already written code. The byproduct is inheriting Python and C/C++ interface mess around dependency management. People needing to write their own algorithms are majority academics and researchers where Julia has flourished. The incentives of corporate work are setup to build on top of Python's ecosystem which means (sadly) Julia will stay in its niche.

hsjqllzlfkf 3y ago

"Just use C++ libraries" is good enough for "simple" operations on "simple" types that have been implemented in C++. If you want C-like performance for generic types without relying on someone having already implemented that in C++, use Julia.

EliasLittle 3y ago

I think you’re missing one of the key points of Julia, that others have also pointed out: you’re reliant on libraries other write in c/c++. Julia’s proposition is that the entire community should be able to read and contribute to these libraries. Unlike the python community where the vast majority of users could not read the source behind those libraries let alone contribute.

Capricorn2481 3y ago
Contributing to libraries may not be a goal for most people
- havercosine 3y ago
  I agree with the observation (most people do not contribute to libraries), but I think the arrow of causality is little different. Python is used as a glue code in scientific computing on top of libraries written in C/C++. The development ergonomics of these languages are intimidating for many people, with both languages having enough footguns. The net result is no-one wants to peek under the hood and see how things are working. A fresher take on scientific computing like Julia, if it is mainstream, might enable more contributions and in general understandability of the black box algorithms.
  - Capricorn2481 3y ago
    Well put
peterdsharpe 3y ago
Generally this doesn't feel like a problem with Python, due to its massive user base advantage. Meta, Google, Microsoft, and many others contribute tens of billions of dollars annually to Python-ecosystem projects (PyTorch, Tensorflow, faster-cpython). Unless that funding differential changes I can't see Julia competing.