DaemonMode.jl
FromFile.jl
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DaemonMode.jl | FromFile.jl | |
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22 | 6 | |
269 | 131 | |
- | - | |
4.7 | 1.5 | |
4 months ago | 12 months ago | |
Julia | Julia | |
MIT License | MIT License |
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DaemonMode.jl
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Potential of the Julia programming language for high energy physics computing
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.
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Julia 1.9.0 lives up to its promise
> If I were to use e.g. Rust with polars, load time would be virtually none.
Because you're compiling...
And if you need to do the same in Julia, you should also pre-compile or some other method like https://github.com/dmolina/DaemonMode.jl (their demo shows loading a database, with subsequent loads after the first one taking roughly ~0.2% of the first)
- Administrative Scripting with Julia
- GNU Octave 8.1
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Ask HN: Why is Julia so underrated?
Well, not nicely certainly, but:
https://github.com/dmolina/DaemonMode.jl
> portable
Neither is python - it just relies on universal availability. Over timeā¦
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Is Julia suitable today as a scripting language?
You can get around a lot of these problems with DaemonMode.jl though.
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Julia performance, startup.jl, and sysimages
You might want DaemonMode.jl
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Can I execute code in Julia REPL if I'm connected to a remote server?
https://github.com/dmolina/DaemonMode.jl can possibly help in the future. Leaving it here so that people know this is planned.
- Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
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Compile for faster execution?
If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.
FromFile.jl
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A Programming language ideal for Scientific Sustainability and Reproducibility?
On include-- you might like FromFile.jl as an alternative.
- Modules in Julia
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How to import an own module from the current directory?
For this and other oddities with Julia's include/import system (and especially as you're coming from Python), I'd recommend FromFile as a readable way to approach things.
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Why not Julia?
You might like FromFile.jl.
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Problems with nested `include`s and solutions?
However, if you prefer a Python-like experience, checkout FromFile.jl
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Julia 1.6: what has changed since Julia 1.0?
I'm not using modules. I usually start with one file with a demo or similarly named function that is called if the file is called as an entry point (like if __name__ == '__main__', except Julia makes it even worse).
I tend to refactor code out of there to separate files, and then somehow import it. An ugly way is include, and I've tried Revise.jl with includet.
But I think the least ugly approach is the @from macro from here: https://github.com/Roger-luo/FromFile.jl Judging from some opinion in bug trackers, this is probably gonna get totally shunned by core devs and they'll keep on bikeshedding about the import stuff forever.
With this setup I have about 400 lines of code in three files. It compiles for 15 seconds. After every single change, and actually without any changes too.
I think performance wise this should be equivalent to using modules, but saving some pointless ceremony.
What are some alternatives?
julia - The Julia Programming Language
Makie.jl - Interactive data visualizations and plotting in Julia
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
HTTP.jl - HTTP for Julia
DataFramesMeta.jl - Metaprogramming tools for DataFrames
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
SymbolicRegression.jl - Distributed High-Performance Symbolic Regression in Julia
TwoBasedIndexing.jl - Two-based indexing
db-benchmark - reproducible benchmark of database-like ops