PythonCall.jl
StaticCompiler.jl
PythonCall.jl | StaticCompiler.jl | |
---|---|---|
3 | 16 | |
681 | 474 | |
4.0% | - | |
8.6 | 6.9 | |
9 days ago | about 1 month ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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PythonCall.jl
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
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Select Python environment for PyCall
https://cjdoris.github.io/PythonCall.jl/stable/ may try this https://cjdoris.github.io/PythonCall.jl/stable/pythoncall/#pythoncall-config
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Making Python 100x faster with less than 100 lines of Rust
You can have your cake and eat it with the likes of
* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl
* NodeCall.jl - https://github.com/sunoru/NodeCall.j
* RCall.jl - https://github.com/JuliaInterop/RCall.jl
I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself
StaticCompiler.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
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Julia App Deployment
PackageCompiler, but it' s a fat runtime and not cross compile. A thin runtime is currently not possible without sacrifices for feature as https://github.com/tshort/StaticCompiler.jl.
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JuLox: What I Learned Building a Lox Interpreter in Julia
https://github.com/tshort/StaticCompiler.jl/issues/59 Would working on this feasible?
- Making Python 100x faster with less than 100 lines of Rust
- What's Julia's biggest weakness?
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Size of a "hello world" application
I just read the project's documentation at https://github.com/tshort/StaticCompiler.jl. It does produce a "hello world" application that is only 8.4k in size 👍. I do like that it can work on Mac OS. Hopefully Windows support will come soon.
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Why Julia 2.0 isn’t coming anytime soon (and why that is a good thing)
See https://github.com/tshort/StaticCompiler.jl
- My Experiences 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.
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We Use Julia, 10 Years Later
using StaticCompiler # `] add https://github.com/tshort/StaticCompiler.jl` to get latest master
What are some alternatives?
jnumpy - Writing Python C extensions in Julia within 5 minutes.
julia - The Julia Programming Language
jsmpeg - MPEG1 Video Decoder in JavaScript
PackageCompiler.jl - Compile your Julia Package
NodeCall.jl - Call NodeJS from Julia.
acados - Fast and embedded solvers for nonlinear optimal control
PyCall.jl - Package to call Python functions from the Julia language
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
oneAPI.jl - Julia support for the oneAPI programming toolkit.
rayon - Rayon: A data parallelism library for Rust
LoopVectorization.jl - Macro(s) for vectorizing loops.