21–30 of 104 posts
• Hello World 200 MB ?
• discoverability of functions:
object.fun => fun(object) in REPL / IDE?
Can I use PyTorch or JAX comfortably in Julia?
are my main concerns resolved: • Hello World 200 MB ? • discoverability of functions: object.fun => fun(object) in REPL / IDE? object. => List of applicable functions?
I think for the most part this is solved. It works very well and has good integration with VS Code:
julia> integrator.
EEst accept_step alg cache
callback_cache differential_vars do_error_check dt dtacc ...
julia> ODEProblem(
ODEProblem(f::SciMLBase.AbstractODEFunction, u0, tspan, args...; kwargs...) @ SciMLBase C:\Users\accou\.julia\dev\SciMLBase\src\problems\ode_problems.jl:183
ODEProblem(sys::ModelingToolkit.AbstractODESystem, args...; kwargs...) @ ModelingToolkit C:\Users\accou\.julia\packages\ModelingToolkit\arrCl\src\systems\diffeqs\abstractodesystem.jl:911
ODEProblem(f, u0, tspan; ...) @ SciMLBase C:\Users\accou\.julia\dev\SciMLBase\src\problems\ode_problems.jl:187
ODEProblem(f, u0, tspan, p; kwargs...) @ SciMLBase C:\Users\accou\.julia\dev\SciMLBase\src\problems\ode_problems.jl:187
Not quite. There's a bunch of knobs you can use to get small binaries (I use this for industrial deployments often), but Jeff Bezanson gave a really nice talk at JuliaCon Local Eindhoven 2023 that described the reasons for the large binaries, what the memory is actually attributed to, and what to do about it (https://youtu.be/kNslvU3WD4M?si=hwo9AgXthNpiQ3-P). With the "normal options" you get to about 15MB now, still bad but not half as bad. The vast majority of that is the base system image. Jeff's talk then goes into the next steps with reducing the size of that base system image.
Im not sure if Julia will ever take off. Right now there are huge investments in the AI space and Julia has no presence in those.
You don't need Julia. Julia was trying to be a better python. We will have better python in form of Mojo.
Mojo also owes part of its design from the lessons it took from Julia (as per Chris Lattner [1]).
Earlier quoted context omitted.
Javascript doesn't compile to native code, so isn't as fast. I've never tried LuaJIT, though, that's supposed to be on par with Julia.
> Javascript doesn't compile to native code, so isn't as fast. There are AOT compilers for JS.
The load time improvements are amazing. Thanks to everyone that was involved. I've been using it locally for months now simply because of this feature and I had to update my "how to deal with compile-time" blog post ( https://sciml.ai/news/2022/09/21/compile_time/ ) to basically say system images really aren't needed anymore with these improvements. With that and the improvements to parallel compilation I tend to not…
using Plots
plot(sin)
Im not sure if Julia will ever take off. Right now there are huge investments in the AI space and Julia has no presence in those.
As much as it pains me to say it, I don't think Julia will. It looks to me like the practical problems with Julia, while addressable, are being addressed too slowly. There is simply too many rough edges and usability problems as it is now, and at the current pace it will take maybe 10 or 15 years to address them. On the other hand, the major use case for Julia is to have a fast, dynamic language. And it seems to me t…
Im not sure if Julia will ever take off. Right now there are huge investments in the AI space and Julia has no presence in those.
As much as it pains me to say it, I don't think Julia will. It looks to me like the practical problems with Julia, while addressable, are being addressed too slowly. There is simply too many rough edges and usability problems as it is now, and at the current pace it will take maybe 10 or 15 years to address them. On the other hand, the major use case for Julia is to have a fast, dynamic language. And it seems to me t…
Earlier quoted context omitted.
As much as it pains me to say it, I don't think Julia will. It looks to me like the practical problems with Julia, while addressable, are being addressed too slowly. There is simply too many rough edges and usability problems as it is now, and at the current pace it will take maybe 10 or 15 years to address them. On the other hand, the major use case for Julia is to have a fast, dynamic language. And it seems to me t…
Python seems to be making rapid strides towards becoming fast
Earlier quoted context omitted.
That’s a broad area. I work in astrodynamic simulations, and don’t know anyone doing much work in Julia. Maybe a couple of grad students playing with it, but that’s it. 99% of the work is Python, Fortran, and C/C++. Are there subdomains that use it a lot? I am not sure what the diffeq landscape is exactly although it sounds related to dynamical simulations?
I'm sure there are other subdomains that make use of Julia, but in particular I think if your problem involves writing an evolution-like or agent-based-model-like simulation you may find the strengths of Julia particularly compelling