Genie.jl VS PackageCompiler.jl

Julia seems to be doing a better job catching up to R in this space than Python. I haven't used it personally, but the demos of Genie Framework are impressive: https://github.com/GenieFramework/Genie.jl / https://genieframework.com/

Hi everyone! I’m Adrian, co-founder of Genie Cloud. Genie Cloud is the no-code platform to quickly build & deploy Julia web apps. It is designed for R&D and data science teams using Julia, who need to share their work with interactive web apps.

Genie Cloud is very simple: import (or write) the Julia code, build the GUI with the drag & drop editor, and deploy the apps in one-click. No frontend code, server stack or hosting to worry about. With Genie Cloud you can build anything, from interactive dashboards to ML demos to production-grade apps.

Genie Cloud is built on top of the open source Genie Framework (https://genieframework.com/), the most popular Julia web framework (I’m also the creator and maintainer of Genie Framework).

At the moment we are in private beta. You can learn more and sign up to get access here: https://www.geniecloud.io/. Looking forward to your thoughts and questions!

I used Julia through my PhD but then started working at a consulting company and had to use Python except for few proof of concepts I built in Julia. Luckily for me, now I'm working at Genie so I finally get to use Julia professionally :)

Have you checked https://genieframework.com/? It's the most popular web dev framework in Julia.

Hi! Genie Builder is a free VSCode plugin that makes it easy to build web GUIs for Julia applications (and in future, Python apps too). Users can simply drag & drop UI elements to create interactive dashboards and data apps, without writing any frontend code.

The tool is designed for data scientists and researchers who need to expose their data models to business users with an interactive web application, but lack the software development skills to build one.

Genie Builder completely eliminates the need to learn frontend development to code the UI. And very soon, we’re also going to support one-click cloud deployments to make it easy to build AND deploy data apps - no frontend nor devops skills required.

I’m Adrian, the creator of the open-source Genie web framework ([https://genieframework.com/](https://genieframework.com/)). Genie offers low-code libraries for building data applications - just like Streamlit or Dash, but for JuliaLang. We developed Genie Builder because of feedback from our open source community who needs more productive data tooling.

Yep, I'm a PHP dev and often do simple JS/jQuery to support my backend code. I have a very general interest in data science and embedded programming, meaning one day I might start doing something with them, but for now, I'm interested in those languages for web development. The following frameworks were especially interesting

Go: https://github.com/gin-gonic/gin

Rust: https://rocket.rs/

Julia: https://genieframework.com/

Check Genie. They're working on an app builder called Genie Cloud.

Yes, julia can be called from other languages rather easily, Julia functions can be exposed and called with a C-like ABI [1], and then there's also various packages for languages like Python [2] or R [3] to call Julia code.

With PackageCompiler.jl [4] you can even make AOT compiled standalone binaries, though these are rather large. They've shrunk a fair amount in recent releases, but they're still a lot of low hanging fruit to make the compiled binaries smaller, and some manual work you can do like removing LLVM and filtering stdlibs when they're not needed.

Work is also happening on a more stable / mature system that acts like StaticCompiler.jl [5] except provided by the base language and people who are more experienced in the compiler (i.e. not a janky prototype)

[1] https://docs.julialang.org/en/v1/manual/embedding/

[2] https://pypi.org/project/juliacall/

[3] https://www.rdocumentation.org/packages/JuliaCall/

[4] https://github.com/JuliaLang/PackageCompiler.jl

[5] https://github.com/tshort/StaticCompiler.jl

One of Julia's Achilles heels is standalone, ahead-of-time compilation. Technically this is already possible [1], [2], but there are quite a few limitations when doing this (e.g. "Hello world" is 150 MB [7]) and it's not an easy or natural process.

The immature AoT capabilities are a huge pain to deal with when writing large code packages or even when trying to make command line applications. Things have to be recompiled each time the Julia runtime is shut down. The current strategy in the community to get around this seems to be "keep the REPL alive as long as possible" [3][4][5][6], but this isn't a viable option for all use cases.

Until Julia has better AoT compilation support, it's going to be very difficult to develop large scale programs with it. Version 1.9 has better support for caching compiled code, but I really wish there were better options for AoT compiling small, static, standalone executables and libraries.

[1]: https://julialang.github.io/PackageCompiler.jl/dev/

Doesn’t work on Windows, but https://github.com/JuliaLang/PackageCompiler.jl does.

Also, you can precompile a whole package and just ship the binary. We do this all of the time.

https://github.com/JuliaLang/PackageCompiler.jl

And getting things precompiled: https://sciml.ai/news/2022/09/21/compile_time/

You can have a look at PackageCompiler.jl

I think by PackageManager here you mean package compiler, and yes these improvements do not need a 2.0. v1.8 included a few things to in the near future allow for building binaries without big dependencies like LLVM, and finishing this work is indeed slated for the v1.x releases. Saying "we are not doing a 2.0" is precisely saying that this is more important than things which change the user-facing language semantics.

And TTFP does need to be addressed. It's a current shortcoming of the compiler that native and LLVM code is not cached during the precompilation stages. If such code is able to precompile into binaries, then startup time would be dramatically decreased because then a lot of package code would no longer have to JIT compile. Tim Holy and Valentin Churavy gave a nice talk at JuliaCon 2022 about the current progress of making this work: https://www.youtube.com/watch?v=GnsONc9DYg0 .

This is all tied up with startup time and are all in some sense the same issue. Currently, the only way to get LLVM code cached, and thus startup time essentially eliminated, is to build it into what's called the "system image". That system image is the binary that package compiler builds (https://github.com/JuliaLang/PackageCompiler.jl). Julia then ships with a default system image that includes the standard library in order to remove the major chunk of code that "most" libraries share, which is why all of Julia Base works without JIT lag. However, that means everyone wants to have their thing, be it sparse matrices to statistics, in the standard library so that it gets the JIT-lag free build by default. This means the system image is huge, which is why PackageCompiler, which is simply a system for building binaries by appending package code to the system image, builds big binaries. What needs to happen is for packages to be able to precompile in a way that then caches LLVM and native code. Then there's no major compile time advantage to being in the system image, which will allow things to be pulled out of the system image to have a leaner Julia Base build without major drawbacks, which would then help make the system compile. That will then make it so that an LLVM and BLAS build does not have to be in every binary (which is what takes up most of the space and RAM), which would then allow Julia to much more comfortably move beyond the niche of scientific computing.

You're probably dancing around the edges of what [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl) is capable of targeting. There are a few new capabilities coming online, namely [separating codegen from runtime](https://github.com/JuliaLang/julia/pull/41936) and [compiling small static binaries](https://github.com/tshort/StaticCompiler.jl), but you're likely to hit some snags on the bleeding edge.