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Julia adoption keeps climbing

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Re: Julia adoption keeps climbing

#11

Post by alephnil »

superbcarrot wrote: post #4

Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…

Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche. In addition to being considerably faster than python, it also has quite some innovative libraries in the area. This can also extend into machine learning, where python has been the go to language, despite its limitations.

For other areas, like web programming, there is no sign of Julia replacing Python in the forseable future.

Re: Julia adoption keeps climbing

#12

Post by carlmr »

jarenmf wrote: post #9

I tested my well-optimised R code and saw only 3x to 10x performance gain. That's still not substantial enough currently to migrate a whole code base, in particular given that the libraries are also still not mature enough. The research group I'm working with also have no interest in adopting anything new, In fact most of our code is still in FORTRAN so that is something I would be more interested in migrating to Jul…

>only 3x to 10x performance gain

That sounds like a lot.

I can see why maturity might be an issue, but after the word only I'd expect something like 5-10%, not integer multiples.

Re: Julia adoption keeps climbing

#13

Post by dfgdghdf »

jarenmf wrote: post #9

I tested my well-optimised R code and saw only 3x to 10x performance gain. That's still not substantial enough currently to migrate a whole code base, in particular given that the libraries are also still not mature enough. The research group I'm working with also have no interest in adopting anything new, In fact most of our code is still in FORTRAN so that is something I would be more interested in migrating to Jul…

I would call that a huge performance gain! Perhaps Julia can be chosen for new projects; it is rarely worth rewriting old ones.

Re: Julia adoption keeps climbing

#14

Post by shiftingleft »

jarenmf wrote: post #9

I tested my well-optimised R code and saw only 3x to 10x performance gain. That's still not substantial enough currently to migrate a whole code base, in particular given that the libraries are also still not mature enough. The research group I'm working with also have no interest in adopting anything new, In fact most of our code is still in FORTRAN so that is something I would be more interested in migrating to Jul…

I guess whether it's effective to migrate a code base depends a lot on where your costs are.

If you pay 500k$ for compute, it might become worthwhile to invest time into rewriting hot paths.

Re: Julia adoption keeps climbing

#15

Post by superbcarrot »

alephnil wrote: post #11
superbcarrot wrote: post #4

Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…

Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche. In addition to being considerably faster than python, it also has quite some innovative libraries in the area. This can also extend into machine learning, where python has been the go to language, despite its limitations. For other areas, like web programming, there is no sign of Julia replacing Python i…

> and is overtaking the python/numpy combo in that niche

No, it's isn't. Julia is growing but it's far from overtaking Python at this point.

> For other areas, like web programming, there is no sign of Julia replacing Python in the forseable future.

That's where Go comes in.

Re: Julia adoption keeps climbing

#16

Post by shiftingleft »

superbcarrot wrote: post #4

Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…

I think this is a fair assessment, and would like to add that the "time to first plot" is also quite the usability issue.

Julia is using LLVM for code-gen has to compile a lot of code before you can actually use stuff like plots.

It takes ages to get a Pluto Notebook up and running, while a jupyter notebook is available instantly.

Re: Julia adoption keeps climbing

#17

Post by sgt101 »

Julia is way superior if you are building programs and systems, especially if you are building for sustained use (rather than something to do a job once). Julia is less error prone, more expressive, more maintainable, more performant.

But if you are creating cut and shut scripts for data science notebooks Python wins... the repl start time alone is a killer for Julia, add in the requirement to actually think about structure and the problem and it's out of my "3 -> 6hr" workflow.

Re: Julia adoption keeps climbing

#18

Post by superbcarrot »

ForHackernews wrote: post #7
superbcarrot wrote: post #4

Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…

You can call Python directly from Julia https://github.com/JuliaPy/PyCall.jl so much of the Python library ecosystem (say, matplotlib) is available to be used in Julia programs. That helps the adoption story quite a bit. You can do the number-crunching in Julia where performance counts, and then analyse and present the results using Python.

- Using Python directly is a better experience than calling Python from Julia

- I've never run into unsolveable performance issues with Python

So I guess I'm not in the target audience unless I just happen to be curious about a new language? That's kind of my overall point - even if Julia is a good language on its own and I work in data science, I don't have reasons to pick it over Python.

Re: Julia adoption keeps climbing

#19

Post by ChrisRackauckas »

jarenmf wrote: post #9

I tested my well-optimised R code and saw only 3x to 10x performance gain. That's still not substantial enough currently to migrate a whole code base, in particular given that the libraries are also still not mature enough. The research group I'm working with also have no interest in adopting anything new, In fact most of our code is still in FORTRAN so that is something I would be more interested in migrating to Jul…

Seems like it would still be useful use Julia as the backend for the R package instead of Fortran. I've been showcasing a lot of that lately with good success:

https://www.stochasticlifestyle.com/juliacall-update-automat...

https://www.stochasticlifestyle.com/gpu-accelerated-ode-solv...

Re: Julia adoption keeps climbing

#20

Post by rademacher »

ForHackernews wrote: post #6
the__alchemist wrote: post #2

Not until it addresses the JIT overhead.

That only matters in a scripting context where you're re-launching over and over as you make small changes. Once you have a real application that's up and running, it just runs.

Julia has some packages to address this now.

https://github.com/timholy/Revise.jl

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