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There's lots of good reasons. Most notably the autodiff in Julia is unreliable -- there's been quite the litany of packages trying/failing to do this robustly a
by patrickkidger 4y ago
There's lots of good reasons. Most notably the autodiff in Julia is unreliable -- there's been quite the litany of packages trying/failing to do this robustly and efficiently. (This issue pretty much kills its possibilities straight from the get-go.)
The fact that the ecosystem is largely created by academics is also a serious issue. Code quality is very low / things silently break / etc. This is partly an issue with Julia itself; the language offers few tools to verify the correctness of your code. (E.g. interfaces etc.)
Julia vs XYZ is an old topic. And yet this keeps coming up! There's quite a lot of blog posts on the topic. (E.g. here's mine: https://kidger.site/thoughts/jax-vs-julia/ https://kidger.site/thoughts/jax-vs-julia/)
- huijzer 4y ago> Code quality is very low / things silently break / etc. Do you have evidence for this claim?
- dagw 4y agoHere is a blog post that was making the rounds recently. https://yuri.is/not-julia/ https://yuri.is/not-julia/ It has many concrete examples of low quality and buggy code making it into pretty core statistics libraries. Most of these bugs are a combination of 'sloppy' coding and lack of vigorous testing of all the corner cases that the language has. Many developers seem to take a more 'academic' approach to things (it works correctly in all the cases I care about) rather than caring about creating robust libraries that are rock solid under all conditions. From the blog: "My conclusion after using Julia for many years is that there are too many correctness and composability bugs throughout the ecosystem to justify using it in just about any context where correctness matters." The good news is that this blog post seems to have brought some light to this problem and hopefully the Julia community can start addressing it.