You can already bundle pre-compiled plotting code in your Julia sys-image if you want. But Julia is not a plotting tool so it would be ridiculous to optimize it just for plotting. I want ODE solvers to have less latency, should I start expecting gnuplot to have built-in ODE solvers or the official installer of Julia to have the ODE libraries pre-compiled?
Maybe this example would make it clearer: why does your argument not apply to Python? Should we expect python libraries to come pre-cached so that the first time I load `sympy` I do not need to wait for tens of seconds to have .pyc files created. Or about matlab?
Again, I am all on board with the idea that julia needs lower latency and if you look at what their devs say, they also agree with that. But expecting Julia to be super low-latency (lower-latency than python/c/matlab) for your pet task is silly.
I gave a proof-of-concept argument as to why something doesn’t need to take as long straight out of the box with no customization. Python is doing it sub-1s. You can also include a non-optimizing interpreter. My point is that being a general purpose language doesn’t inherently limit you in any way; instead it’s one’s choice of implementation strategy.
Another strategy: when a user installs Julia, they select “fast-loading” libraries. You’d be surprise how small changes in UI/UX make huge perceived differences in quality and performance. I bet “Julia can already do this” too, but nobody does it because it’s not idiomatic and it’s not recommended up front.
At the end of the day, people don’t complain about Python or MATLAB as much because they feel nicer. If it feels nicer because of some other reason than absolute time, then they’re doing something about UX that Julia is not, because everybody really does feel Julia is extremely sluggish to use.
One thing that would help a lot is if julialang.org offered downloads with a bunch of common packages baked into a sysimage (modeled roughly of of anaconda). My rough estimate of packages to include would be
Plots, BandedMatrices, DifferentialEquations, CSV, DataFrames, Flux, PyCall, (and probably some others). Having a distribution with a lot of the heavy hitter built in would make workflows using those packages much snappier.
>Another strategy: when a user installs Julia, they select “fast-loading” libraries. You’d be surprise how small changes in UI/UX make huge perceived differences in quality and performance. I bet “Julia can already do this” too, but nobody does it because it’s not idiomatic and it’s not recommended up front.
Don't most people already use a bit of PackageCompiler.jl in their workflows? Maybe it just need to be mentioned more in introduction videos, but it's literally one line of code to do this and I don't see how that isn't the solution already. Most users I know have been doing it for a few years now.
For reference, with Plots and DifferentialEquations the command is:
using PackageCompiler; create_sysimage([:Plots,:DifferentialEquations])
It's simple enough to add to every tutorial. I think the issue is that intro videos just need to be updated.
I'm new to Julia. I had heard of PackageCompiler, but assumed that was only for creating redistributable binaries for others. I also assumed `]precompile` was the best I could do to speed up libraries.
My suggestions: 1- please forget about the intro videos. Those are impossible to keep up-to-date, and are not a good reference resource. Sure, a future video should explain PackageCompiler, but that is not sufficient. 2- please update docs.julialang.org with all such guidelines. This is the likely the first place people look, so if there are important guidelines that everyone could benefit from, this is where they belong.
Maybe this example would make it clearer: why does your argument not apply to Python? Should we expect python libraries to come pre-cached so that the first time I load `sympy` I do not need to wait for tens of seconds to have .pyc files created. Or about matlab?
Again, I am all on board with the idea that julia needs lower latency and if you look at what their devs say, they also agree with that. But expecting Julia to be super low-latency (lower-latency than python/c/matlab) for your pet task is silly.