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I think I may have missed what alternative is being recommended instead, after scrolling through the whole article.

by TheRealPomax 1y ago

wbolt 1y ago
None
taikahessu 1y ago
Hinted as Python not having these issues.
- TimorousBestie 1y ago
  Python also suffers from such problems. For example, here’s scipy’s issue tracker, filtered for bugs only: https://github.com/scipy/scipy/issues?q=is%3Aissue%20state%3Aopen%20BUG https://github.com/scipy/scipy/issues?q=is%3Aissue%20state%3... Scrolling through this list, it’s clear that many are “correctness issues.” I do not link this to argue that scipy bugs are more serious or more frequent. I don’t think that kind of statistical comparison is meaningful. However, I think a motivated reasoner could write a very similar blog post to the OP, but about $arb_python_lib instead of $arb_julia_lib. I suppose my position is closer to “only Kahan, Boyd and Higham write correct numerical algorithms” (a hyperbole, but illustrative of the difficulty level).
  - Grikbdl 1y ago
    Explicit errors are not correctness issues. Some numerical instability issues in certain algorithms in certain corner cases might be considered as such though. Regardless, overall, these are grossly of another complexity and seriousness than the base sum function being just wrong, or cultural issues among developers with not verifying inputs "for performance", or things of that nature. The scientific Python community has, in my experience, a much higher adherence to good standards than that.
    - TimorousBestie 1y ago
      > Explicit errors are not correctness issues. Yes, of course. I am not conflating the two. > The scientific Python community has, in my experience, a much higher adherence to good standards than that. Not in my experience. Nor am I defending Julia.
    - postflopclarity 1y ago
      I have found multiple "correctness bugs" of equal seriousness in Polars, and one of them is still open. that is not to throw shade at polars --- I love that package! but my point is that these things happen everywhere in software.
cs702 1y ago
The OP shows examples of people being unable to solve a problem in Julia that they solve quickly after switching to PyTorch, Jax, or TensorFlow, so the OP is implicitly recommending those alternatives.
- DNF2 1y ago
  But those are not languages, but frameworks, and are not general enough to solve many problems, especially outside of machine learning.
umvi 1y ago
He doesn't recommend any alternatives. Looking at his GitHub profile (https://github.com/yurivish?tab=repositories https://github.com/yurivish?tab=repositories), looks like he's using a lot of Rust and Go these days, though looks like mainly for projects unrelated to the sorts of data crunching suitable for Julia, R, etc
acomjean 1y ago
We’ve had a lot of scientist use R and the “tidyverse” collection of packages. Ggplot2 is fantastic for graphing. I thing a lot of them used “rstudio” to browse the data. https://www.tidyverse.org/ https://www.tidyverse.org/

I think I may have missed what alternative is being recommended instead, after scrolling through the whole article.

wbolt 1y ago

None

taikahessu 1y ago

Hinted as Python not having these issues.

TimorousBestie 1y ago
Python also suffers from such problems. For example, here’s scipy’s issue tracker, filtered for bugs only: https://github.com/scipy/scipy/issues?q=is%3Aissue%20state%3Aopen%20BUG https://github.com/scipy/scipy/issues?q=is%3Aissue%20state%3... Scrolling through this list, it’s clear that many are “correctness issues.” I do not link this to argue that scipy bugs are more serious or more frequent. I don’t think that kind of statistical comparison is meaningful. However, I think a motivated reasoner could write a very similar blog post to the OP, but about $arb_python_lib instead of $arb_julia_lib. I suppose my position is closer to “only Kahan, Boyd and Higham write correct numerical algorithms” (a hyperbole, but illustrative of the difficulty level).
- Grikbdl 1y ago
  Explicit errors are not correctness issues. Some numerical instability issues in certain algorithms in certain corner cases might be considered as such though. Regardless, overall, these are grossly of another complexity and seriousness than the base sum function being just wrong, or cultural issues among developers with not verifying inputs "for performance", or things of that nature. The scientific Python community has, in my experience, a much higher adherence to good standards than that.
  - TimorousBestie 1y ago
    > Explicit errors are not correctness issues. Yes, of course. I am not conflating the two. > The scientific Python community has, in my experience, a much higher adherence to good standards than that. Not in my experience. Nor am I defending Julia.
  - postflopclarity 1y ago
    I have found multiple "correctness bugs" of equal seriousness in Polars, and one of them is still open. that is not to throw shade at polars --- I love that package! but my point is that these things happen everywhere in software.

cs702 1y ago

The OP shows examples of people being unable to solve a problem in Julia that they solve quickly after switching to PyTorch, Jax, or TensorFlow, so the OP is implicitly recommending those alternatives.

DNF2 1y ago
But those are not languages, but frameworks, and are not general enough to solve many problems, especially outside of machine learning.

umvi 1y ago

He doesn't recommend any alternatives. Looking at his GitHub profile (https://github.com/yurivish?tab=repositories https://github.com/yurivish?tab=repositories), looks like he's using a lot of Rust and Go these days, though looks like mainly for projects unrelated to the sorts of data crunching suitable for Julia, R, etc

acomjean 1y ago

We’ve had a lot of scientist use R and the “tidyverse” collection of packages. Ggplot2 is fantastic for graphing. I thing a lot of them used “rstudio” to browse the data. https://www.tidyverse.org/ https://www.tidyverse.org/