Yes, this has been discussed a lot recently in the Julia community and some efforts have been made to make it happen, there will be more about this in the 1.11 release. Standalone binaries are next.
If this is fixed, I'll probably switch from Python to Julia. As a language Julia is vastly superior to Python. The complaint was dismissed for so long (and is still dismissed e.g. here in HN discussions) I gave up hope it will be fixed, but I'd be ecstatic to be proven wrong.
Do standalone binaries here include shared libraries (with a C ABI)? That would be a dream.
The one you need is this which is already merged but was after the 1.10 feature freeze so it has to wait till 1.11, though you can test it with nightly builds which is available on julialang site: https://github.com/JuliaLang/julia/pull/51435
Unfortunately, the core devs are not too chatty about standalone binaries, because of how Julia's internals are set there are going to be a lot of unforeseen challenges, so they are not trying to promise how things will be rather let's wait and see how things will turnout. Since packagecompiler.jl already has C ABI and one goal discussed about binaries being easily callable from other languages and vice versa, I would bet that it will have shared libraries.
> The one you need is this which is already merged but was after the 1.10 feature freeze so it has to wait till 1.11, though you can test it with nightly builds which is available on julialang site: https://github.com/JuliaLang/julia/pull/51435
The discussion seems to be too deep in Julia internals for me to follow. Is this about startup time or defining an entry point (or both?). I haven't had problems with Julia entrypoints (yet at least).
With the nightly `./julia-f7618602d4/bin/julia -e "using DynamicalSystems"` still takes over 5 seconds. Can I somehow define a main to make this faster or precompile more efficiently?
> Since packagecompiler.jl already has C ABI and one goal discussed about binaries being easily callable from other languages and vice versa, I would bet that it will have shared libraries.
Sounds promising. Shared libraries are not a musthave for me, but could allow Julia save us from C++ in more cases.
Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.
DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.
You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.
The half-life of major Julia problems is impressively short.
Compiler latency ("time to first plot") used to be miserable but after a few releases with incremental improvements it feels mostly solved to me.
Just now on Friday at JuliaCon Local in Eindhoven one of the keynotes was about similar ongoing work on stand-alone binaries (including shared libraries to call like C/Fortran.)
Some of what you have written seems pre 1.0 release and some pre 1.9. I have never seen anybody in the community say the correct way to use Julia is in a notebook. As far as I have seen, some people use a simple editor and have the REPL open, and most just use it in vscode.
You can do real-time applications just fine in Julia, just preallocate anything you need and avoid allocations in the hot loop, I am doing real-time stuff in Julia. There are some annoyances with the GC but nothing to stop you from doing real-time. There are robotics packages in Julia and they are old, there is a talk about it and compares it with c++(spoiler, developing in julia was both faster and easier and the results were faster).
I have been using two Julia sessions on an 8gb laptop constantly while developing, no problem. LSP loads fine and fast in vscode no problem there either.
The debugger in vscode is slow and most don't use it. There is a package for that. The big binaries are a problem and the focus is shifting there to solve that. Stacktrace will become much better in 1.10 but still needs better hints(there are plans for 1.11). In general, we need better onboarding documentation for newcomers to make their experience as smooth as possible.
The recommended solution to slow startup times has always been keep a repl open. That's basically the same workflow as a notebook in my mind. Like I said this means there is a large class of tasks julia a doesn't make sense for because paying the startup cost is too expensive compared to python or go.
I just timed vscode with the lsp. From the point I open a 40 line file of the lorenz attractor example, it takes 45 seconds until navigation within that same file works, and the lsp hogs 1 GB of memory. That's 5x the memory of clangd and 20x worse performance; hardly what I would consider a snappy experience.
I have no doubt that julia can be shoe-horned into realtime applications. But when I read threads like this [1], it's pretty clear that doing so amounts to a hack (e.g., people recommending that you somehow call all your functions to get them jited before the main loop actually starts). Even the mitigations you propose, i.e., pre-allocating everything, don't exploit any guarantees made by the language, so you're basically in cross-your-fingers and pray territory. I would never feel comfortable advocating for this in a commercial setting.
I don't know man, I just tested vscode and it's almost instant, loads every function from multiple files in less than 5 seconds. I'm on a 13-inch intel Mac and Julia 1.11 master (1.9 and 1.10 should be the same).
Having a REPL open is not the same thing as a notebook, if you feel like that, cool I guess.
That thread is old and Julia can cache compiled code now from 1.9 and onward. However, it can not distribute the cached code(yet).
Writing the fastest possible real-time application in c/c++ has the same principles as in Julia. It's not as shoe-horned as you might believe.
When developing Julia, the developers chose some design decisions that affected the workflow of using the language. If it doesn't fit your needs that's cool, don't use it. If you are frustrated and like to try the language come to discourse, people are friendly.
>I don't know man, I just tested vscode and it's almost instant, loads every function from multiple files in less than 5 seconds. I'm on a 13-inch intel Mac and Julia 1.11 master (1.9 and 1.10 should be the same).
I know, I'm always "holding it wrong". And that's the problem with julia.
> Having a REPL open is not the same thing as a notebook, if you feel like that, cool I guess.
Both workflows amortize the JIT times away by keeping an in-memory cache compiled code. This makes a lot of smaller scripting tasks untenable in julia. So people chose python instead. That means julia needs a massive advantage elsewhere if they are going to incorporate both languages into their project.
> When developing Julia, the developers chose some design decisions that affected the workflow of using the language. If it doesn't fit your needs that's cool, don't use it. If you are frustrated and like to try the language come to discourse, people are friendly.
This thread was about why julia hasn't seen wider adoption. It's my contention that the original design decisions are a one of the root causes of that.
I just tried it from the Windows command line and this benchmark with the plots ran in what seemed like instant, and some simple timing showed it was under 2 seconds with a fresh Julia v1.10 beta installation. That seems to line up with what amj7e is saying, and I don't think anyone would call the Windows command line the pinnacle of performance? That's not to say Julia's startup is fast, but it has improved pretty significantly for workflows like due to the package caching. It needs to keep improving, and the work to pull OpenBLAS safely out of the default system image will be a major step in that direction, but it's already almost an order of magnitude better than last year in most of the benchmarks that I run.
I think the person you are replying to was using notebook as shorthand for interactively. You don't write scripts that you call, you have to have a repl open to interactively feed code to.
Autocompletion in Julia is also just terrible and the tooling really is lacking compared to better funded languages. No harm in admitting that (when Julia had no working debugger some people were seriously arguing that you don't need one: Just think harder about your code! Let's please bury that attitude...)
This certainly has not been my experience with Julia people. Sure there are opinionated people in every community, but most of pain points are acknowledged and known.
I can confirm that there were multiple (heated) arguments on Discourse, where some posters completely dismissed the need for debuggers in general. I remember it quite well.
It was very strange, but I don't think it says anything about the community, except that people have different opinions and preferences, like in any community.
>> For a long time, one got told that the "correct" way to use julia was in a notebook. Outside of that, nobody wanted to hear your complaints.
> I have never seen anybody in the community say the correct way to use Julia is in a notebook.
patrick's comment is fully in the past tense for this part, and that was indeed a pretty common thing for a long while in the past. Especially pre-1.0, before Revise became mature and popular, an oft-recommended workflow was to use a notebook instead of an editor. Or it would come in the form of a reply to criticism about the slow startup or compilation latency - "the scientists who this language is made for use notebooks anyway, so it doesn't matter" was a common response, indirectly implying that that was the intended way to use the language.
You must mean REPL, not notebook. I've been following the community since before the move to Discourse, and "use the REPL" surely outnumbers "use a notebook" by orders of magnitude.
Here on HN (in threads about Julia) the focus was generally on notebooks as I remember it. That's the context I assumed, but if it's about Julia fora in general I agree, the REPL had/has been talked about much more often than notebooks.
Only if it's the Correct opinion. Try being a julia user but thinking julia kinda sucks. That's a much more hostile experience than being a c++ dev and thinking c++ kinda sucks.
It's really all down to your tone and attitude. If you're hostile, demanding and negative, you will indeed get pushback, but that's human nature.
Some people feel that they should get to act like a prick, while everyone else should be humble and courteous.
If you act like a decent person person, there's no problem pointing out weak points in Julia and request help to work around it. If your only input is "Julia kinda sucks", what kind of feedback do you feel that you are owed?
There was a recent post in Julia's Discourse about why people think the language has not caught on as much as it should. There were around 550 comments where half of them talked about why Julia sucks and what to do about it, there are spin-offs of the post continuing discussion. Let's just cut the bull shit, these are all tools, if one doesn't fit you just use the other.
Yeah, but it was just a project.toml update. There haven't been serious updates to the src for a long time that's why Im asking. Hopefully, it resumes.
