I have a package that my collaborators want to use like a script. Specifically, they want it to load quickly. Once it’s compiled, my part of the code is fast — around 0.01s, compared to the several seconds taken by my equivalent Python package. But loading the code still takes almost 30 seconds, …

@MilesCranmer may have some advice on this. See [image] GitHub - MilesCranmer/PySR: High-Performance Symbolic Regression in Python... High-Performance Symbolic Regression in Python and Julia - GitHub - MilesCranmer/PySR: High-Performance Symbolic Regression in Python and Julia …

Depending on the use case, GitHub - dmolina/DaemonMode.jl: Client-Daemon workflow to run faster scripts in Julia can be useful. (just to note, on my laptop those two using take ~5 and ~3 seconds, after the long installation / precompilation times, in a fresh environment).

Thanks for the tip about DaemonMode. It looks like something that could come in handy for me. Unfortunately, I don’t think my colleagues will ever use my code if they have to do that. Also, I should add that one of my colleagues has a newer M1 Mac, and reports that it takes him almost 30 seconds …

As a side note, using OrdinaryDiffEq on my Desktop (Ryzen 7850X) using Julia master takes 1.5 seconds… This is by a factor of 1.6 faster then with Julia 1.9.

PrecompileTools is not automatic and many packages have not adopted it yet. Is is not clear to me if native caching here is slowing you down or if it is not being used extensively enough. If this does not work, you can also fallback to the older solution: https://julialang.github.io/PackageCompile…

PrecompileTools is not automatic and many packages have not adopted it yet. Is is not clear to me if native caching here is slowing you down or if it is not being used extensively enough. Looks like OrdinaryDiffEq makes extensive use of precompilation . And there is a lot of it. But that chunk o…

Have you read this post? You may not need to load all of OrdinaryDiffEq. My package does actually use a solver from OrdinaryDiffEq, so I think I really need it. Thanks, though.

You probably only need one solver right? The preferences allow you to turn on or off compilation if specific solvers, turn it off for those you don’t use. :blush:

https://docs.sciml.ai/DiffEqDocs/stable/features/low_dep/#Controlling-Function-Specialization-and-Precompilation

[image] moble: Again, I feel like that’s asking more of my prospective users than they would likely stomach. For end user applications, I usually provide a script that performs the package compiler step as part of “installation”. I did some analysis of the loading time for these two packages…

Any way to speed up loading large precompiled packages?

General Usage

PeterSimon May 13, 2023, 3:14am 6

Have you read this post? You may not need to load all of OrdinaryDiffEq.

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Any way to speed up loading large precompiled packages?

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