dmolina

DaemonMode.jl

Julia2020–2023

dmolina / DaemonMode.jl

DaemonMode.jl

Client-Daemon workflow to run faster scripts in Julia

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12Distinct people

22Mentions

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305GitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

Client-Daemon workflow to run faster scripts in Julia

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

julialang/packagecompiler.jl · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year22 dated mentions

Observed mentions: 2020-08-03 18:32:29 UTC to 2023-12-04 17:26:11 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	2	2
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	11	11
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	1	1
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	8	8

What Hacker News says

22 sources

amj7e·Dec 4, 2023

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.

In Potential of the Julia programming language for high energy physics computing

Nanana909·May 14, 2023

> 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)

In Julia 1.9.0 lives up to its promise

Nanana909·May 14, 2023

Then precompile or use https://github.com/dmolina/DaemonMode.jl. But 1.7 seconds at first startup isn't even enough time to articulate a serious thought, much less write any good code. I struggle to believe it's a dent in anyones workflow.

In Julia 1.9.0 lives up to its promise

Nanana909·May 14, 2023

I'll preface by saying by far my biggest gripe with Julia is the inability to deploy binaries easily. The only hope on easy deployment I suppose is to hope it becomes commonly installed enough that you can rely on it being on systems. But some of your complaints are a bit off (in my own opinion): >and 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 disagr

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ee. Of course you can - that's how many of use Julia. > 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. These languages are all tools to translate mathematics into computer instructions. The code should then ideally 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. If you're working in these or related fields at all and write any code with Julia, it's hard to not fall in love. Mojo may make Python faster. But it will still be, roughly, Python. And considering that the Python 2 -> Python 3 transition has still not completed, I think it's premature to conclude Mojo's python upgrades will get anywhere.

In Julia 1.9.0 lives up to its promise

sundarurfriend·May 13, 2023

> Making a "busybox" style executable that replaces all coreutils I wonder if DaemonMode (https://github.com/dmolina/DaemonMode.jl) is the right approach for this, having each of those tools' functionality loaded in the server process, and calling out to them from a client when a tool is invoked. > a complete base package for sparse matrices, including kronecker products etc. SparseArrays is a standard library package for sparse matrices and arrays. kron is in the LinearAlgebra standard library. So you can just do using LinearAlgebra using SparseArrays and then do any kron product you want, whether sparse matrices with other sparse matrices, or dense ones, or any other array type.

In Julia 1.9.0 lives up to its promise

Nanana909·Apr 7, 2023

Reduce Julia startup/JIT time for scripts with https://github.com/dmolina/DaemonMode.jl

In Administrative Scripting with Julia

Nanana909·Mar 21, 2023

For one off Julia scripts consider using https://github.com/dmolina/DaemonMode.jl

In GNU Octave 8.1

Nanana909·Mar 21, 2023

> CLI Well, not nicely certainly, but: https://github.com/dmolina/DaemonMode.jl > portable Neither is python - it just relies on universal availability. Over time… > Multiple dispatch, it's system of abstract inheritance and lack of static analysis and tooling makes it very difficult to write correct code. It's even hard to read Julia code due to dispatch labyrinths, macros that obfuscate IDEs, and lack of good IDE tooling. There is support for solving some of this but it’s obscure and needs more attention. Overall I think the growth will continue, specific fields having best of any language software packages (DifferentialEquations.jl,…) written in Julia starts to create somewhat of a network effect. And imo the packages being written in Julia are quite specialized, unlike much of the famed “Python ecosystem”, half of which is wrapper to

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C++ functions and another big chunk being HTTP calls to external APIs..

In Ask HN: Why is Julia so underrated?

not-my-account·Sep 12, 2022

WRT boot times, yep it can take a little bit. There are packages like revise [0] or daemon mode [1] that ameliorate this issue while developing the code- and if it is a long running script like DL training, a long startup time will not matter. https://github.com/timholy/Revise.jl https://github.com/dmolina/DaemonMode.jl

In Ask HN: Why hasn't the deep learning community embraced Julia yet?

dunefox·Dec 18, 2021

There's https://github.com/dmolina/DaemonMode.jl.

In Which programming language or compiler is faster

shele·Jun 22, 2021

Just keep it running as a daemon? https://github.com/dmolina/DaemonMode.jl

In Is Julia Really Fast?

eigenspace·Jun 20, 2021

While I definitely agree, I'd say Julia is totally fine for short scripts too. Just spin up https://github.com/dmolina/DaemonMode.jl and you can launch lots of short scripts without excessively restarting julia. There's also PackageCompiler.jl https://github.com/JuliaLang/PackageCompiler.jl for AOT compilation, but that's heavy enough that I wouldn't bother with it for short scripts.

In Julia: Faster than Fortran, cleaner than Numpy

eigenspace·Jun 20, 2021

You can have what you want here by using DaemonMode.jl https://github.com/dmolina/DaemonMode.jl

In Julia: Faster than Fortran, cleaner than Numpy

eigenspace·Jun 20, 2021

The startup times have improved a lot in version 1.6, the latest release, and are I Proving again in the next upcoming release candidate. Are you hitting the startup time very often? If so, you might want to try some things to keep one julia session open and sending code to it like a daemon instead of constantly closing and opening sessions. This package makes that workflow really easy: https://github.com/dmolina/DaemonMode.jl

In Julia: Faster than Fortran, cleaner than Numpy

pdeffebach·Apr 29, 2021

Plenty of people use the REPL in terminal and sublime text or vim or whatever. I also dislike browser-based tooling and think Julia has done a good job avoiding Rstudio-style dependencies. But if your point is the inability to do `julia script.jl` , yeah thats a pain point. Fortunately there has been some tooling to make running many jobs in a row easier: https://github.com/dmolina/DaemonMode.jl

In Introduction to Pluto.jl

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