DaemonMode.jl
HTTP.jl
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DaemonMode.jl | HTTP.jl | |
---|---|---|
22 | 7 | |
268 | 619 | |
- | 0.5% | |
4.7 | 7.7 | |
3 months ago | 4 days ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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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
>a nd you can't quickly run a script
What is wrong with the following to run a script?
$ julia myscript.jl
If you have specific needs that demand, after hitting return, the few seconds of delay for the vast majority of scripts is an issue, you can pre-compile it ahead of time or simply use something like https://github.com/dmolina/DaemonMode.jl
Julia has issues as with all languages but "not being able to quickly run a script" is by far one of the easiest to work around.
> and you can't quickly run a script or REPL for development.
REPL- I'm not sure what you are getting at here. Of course you can - that's how many of use it.
> And now Julia has competition from Mojo.
...maybe. The code-samples we've seen from Mojo look very similar to Python, obviously. And that is specifically why a lot of poeple love Julia.
The problems people are more and more interested in (machine learning, etc) are at their base mathematical problems. The code should look as close to that math as possible. Spamming np.linalg, sp.sparse, and so forth over and over again is just ugly, and the entire Python workflow overly encourages object oriented design for concepts that are mathematically functions. And, well, should be functions.
Mojo may make Python faster, but even with Mojo, Python will always be a high level wrapper around C and C++.
> 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
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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.
HTTP.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
The req.url field contains the URL of the received request, the req.method field contains request method, like GET or POST, the req.body field contains the POST body of the request in binary format. HTTP request object contains much other information. All this you can find in HTTP.jl documentation. Our web application will only check the request method. If the received request is a POST request, it will parse req.body to JSON object and send the data from this object to the isSurvived function to make a prediction and return it to the client browser. For all other request types, it will just return the content of the index.html file, to display the web interface. This is how the whole source of titanic.jl web service looks:
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Automate the boring stuff with Julia?
HTTP.jl and Gumbo.jl for web-scraping
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Recommendations on how to start web scraping with julia for price updates? (if possible)
I haven't seen that tutorial, but I agree that HTTP.jl, Gumbo.jl, and Cascadia.jl are the way. I used them to export public wishlists from bookdepository, which has no API nor a built in exporting tool.
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Why not Julia?
I find some of the library documentation hard to understand. Compare http.jl with python's requests, for example. Something as core as HTTP requests should have clear docs with tonnes of examples. Part of this is also a personal dislike of documenter.jl styling. Idk why the contrast is so low – would prefer a standard readthedocs theme.
- Julia 1.6: what has changed since Julia 1.0?
What are some alternatives?
julia - The Julia Programming Language
Makie.jl - Interactive data visualizations and plotting in Julia
geni-performance-benchmark
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
PackageCompiler.jl - Compile your Julia Package
BinaryBuilder.jl - Binary Dependency Builder for Julia
Gumbo.jl - Julia wrapper around Google's gumbo C library for parsing HTML
RCall.jl - Call R from Julia