gopy
PackageCompiler.jl
gopy | PackageCompiler.jl | |
---|---|---|
5 | 26 | |
1,868 | 1,371 | |
1.1% | 0.5% | |
6.7 | 7.8 | |
3 days ago | 3 days ago | |
Go | Julia | |
BSD 3-clause "New" or "Revised" License | MIT License |
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gopy
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Making Python 100x faster with less than 100 lines of Rust
I've used gopy[0] recently to access a go library in Python. It surprisingly Just Worked, but I was disappointed by some performance issues, like converting lists to slices.
[0] https://github.com/go-python/gopy
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Golang vs python for AI
the heavy lifting is done in native libraries and you get to experiment fast using an easy language. the combo is quite hard to beat. Now there is a missed opportunity to write such libraries in Go, but as I read here and there Go is hard to integrate well as a library. There is gopy but it's light years away from PyO3 for instance, I don't think it'll ever gain traction, but who knows.
- Is the statement true, that Python and its ecosystem lacks speed for mission-critical large-scale applications?
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I went about learning Rust
> So if you learn Go, you'll never be able to use it to interoperate with e.g. your Python program to speed it up.
Never done it myself, but:
https://www.ardanlabs.com/blog/2020/07/extending-python-with...
https://github.com/go-python/gopy
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Rust or C/C++ to learn as a secondary language?
Check out gopy for an easy way to extend your Python code with Go.
PackageCompiler.jl
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Potential of the Julia programming language for high energy physics computing
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
- Strong arrows: a new approach to gradual typing
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Making Python 100x faster with less than 100 lines of Rust
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/
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What's Julia's biggest weakness?
Doesn’t work on Windows, but https://github.com/JuliaLang/PackageCompiler.jl does.
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I learned 7 programming languages so you don't have to
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/
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Julia performance, startup.jl, and sysimages
You can have a look at PackageCompiler.jl
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Why Julia 2.0 isn’t coming anytime soon (and why that is a good thing)
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.
- Is it possible to create a Python package with Julia and publish it on PyPi?
- GenieFramework – Web Development with Julia
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Julia for health physics/radiation detection
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.
What are some alternatives?
PySCIPOpt - Python interface for the SCIP Optimization Suite
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
Pulumi - Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀
julia - The Julia Programming Language
prisma-engines - 🚂 Engine components of Prisma ORM
Genie.jl - 🧞The highly productive Julia web framework
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
LuaJIT - Mirror of the LuaJIT git repository
cpy3 - Go bindings to the CPython-3 API
Dash.jl - Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.
PythonCall.jl - Python and Julia in harmony.
Transformers.jl - Julia Implementation of Transformer models