31–40 of 216 posts
Earlier quoted context omitted.
Most likely a single or dual core CPU. I have similar compilation times with Rust on an old Asus 1215B, where 8GB and SSD hardly help the compile the world from scratch cargo model, when starting a new project.
reasonably simple notebook != compile the world from scratch
Matlab users should switch to Julia. It’s a real programming language, and better in many ways. I provide the option of Julia in my tutorials. Students are lazy, and don’t want to explore something new. Most of them stick with matlab. What prevents matlab users from switching? The syntax is similar.
I also did this and only the students that were well ahead of the rest gave it a try. But I think this is natural, many students are already pushed to the limit to manage the current set of studies. Learning a whole new language on top of that introduces a lot of extra risk and has an opportunity cost that might not make it feasible (unless you are already doing very well).
I don't think it is so much that students are lazy but more that the current way that the study plan is made in universities doesn't allow for much risk taking by the students. So they will just go the "cookie cutter" way.
Matlab users should switch to Julia. It’s a real programming language, and better in many ways. I provide the option of Julia in my tutorials. Students are lazy, and don’t want to explore something new. Most of them stick with matlab. What prevents matlab users from switching? The syntax is similar.
Syntax is one thing, but as others have mentioned it's more than that. Libraries, tooling, IDE and perhaps also knowing the ideosyncracies and pitfalls, and how to recover from them. I'm very comfortable in Matlab and often know immediately what's wrong when I hit those oddities. In Python it usually means I spend considerable time googling and tinkering before I even understand what I did wrong because I have far le…
I have made a career in python data science since then overall it was a very good decision for me. I was ahead of the curve when data science popped up.
I have since joined a company with engineering dept that rely on matlab. I have no doubt that they wouldn’t get much benefit from switching to python or Julia apart from the license costs.
That's big! Now I can add packages to my startup.jl without having to worry that every single REPL startup will be slowed down by them. This also eases the pain of things being moved away from the standard library, since we can just add them back to the base environment and load them at startup, making it basically the same thing.
Matlab users should switch to Julia. It’s a real programming language, and better in many ways. I provide the option of Julia in my tutorials. Students are lazy, and don’t want to explore something new. Most of them stick with matlab. What prevents matlab users from switching? The syntax is similar.
Choosing a programming language based on just comparing the language syntax only works for academic settings or toy projects for self-curiosity and learning. Once you consider adopting a language for complicated real-world industry usage, you have to look beyond the syntax and compare ecosystem to ecosystem.
E.g. Look over the following MATLAB "toolboxes" and add-ons developed over decades: https://www.mathworks.com/products.html
Julia doesn't have a strong equivalent ecosystem for many of those. In that MATLAB product list is Simulink. Tesla uses that tool to optimize their cars: https://www.mathworks.com/company/newsletters/articles/using...
You can take a look at some of the 1-minute overview videos to get a sense of MATLAB toolboxes that companies pay extra money for: https://www.youtube.com/results?search_query=discover+matlab...
It has add-ons such as medical imaging toolkit, wireless communications (antenna signal modeling), etc. And MATLAB continues releasing new enhancements that the Julia ecosystem doesn't keep up with.
If one doesn't need any of the productivity tools that MATLAB provides, Julia becomes a more realistic choice.
Or to put it another way, companies didn't really "choose the MATLAB programming language". What they really did was choose the MATLAB visual IDE and toolkits -- which incidentally had the MATLAB programming language.
Matlab users should switch to Julia. It’s a real programming language, and better in many ways. I provide the option of Julia in my tutorials. Students are lazy, and don’t want to explore something new. Most of them stick with matlab. What prevents matlab users from switching? The syntax is similar.
It's got a frustrating "not fun" on-boarding. ie. the number of minutes from downloading "Julia" to getting cool satisfying results
1. It not a calculator on steroids like Matlab. It doesn't have one main open source IDE like Octave/Rstudio that you can drop in and play around in (see plots docs repl workspace)
2. The default language is more like a "proper programming language". To even make a basic plot you need to import one of a dozen plotting libraries (which requires learning how libraries and importing works - boring ..) and how is someone just getting started to decide which one..? I don't need that analysis paralysis when I'm just getting started
3. Documentation .. Well it's very hard to compete with Matlab here - but the website is not as confidence inducing. The landing page is a wall of text: https://docs.julialang.org/en/v1/ Tbh, from the subsequent manual listing it's not even clear it's a math-focused programming language . It's talking about constructors, data types, ffi, stack traces, networking etc etc.
"Together with PrecompileTools.jl, Julia 1.9 delivers many of the benefits of PackageCompiler without the need for user-customization." does it mean I still have to invoke special workflows and commands to get compilation benefits or does it work out of the box for normal julia invocations?
PrecompileTools works out of the box - in the sense that the package developer needs to add a "@compile_workload" block in their package, but the users don't need to do anything. There is no special workflow or command to use it. The tradeoffs are somewhat larger load times (TTL), increased precompilation time (because some of the compilation moves to precompile time), and increased disk usage by the package.
The post says "TTL has also been reduced, albeit not as dramatically as TTFX." And the graph seems to indicate the same. Is that not true, or are you comparing it to pre-1.7 TTLs (which are not shown in the post), or is it just context(/project)-dependent?
Earlier quoted context omitted.
Most likely a single or dual core CPU. I have similar compilation times with Rust on an old Asus 1215B, where 8GB and SSD hardly help the compile the world from scratch cargo model, when starting a new project.
reasonably simple notebook != compile the world from scratch
Matlab users should switch to Julia. It’s a real programming language, and better in many ways. I provide the option of Julia in my tutorials. Students are lazy, and don’t want to explore something new. Most of them stick with matlab. What prevents matlab users from switching? The syntax is similar.
I don't know if you genuinely want feedback... But I'll share my very short experience. I tried Julia one time a few years back. I'll be honest, I didn't put in a lot of effort into (but nor will most potential Matlab converts - bc people are busy and have stuff to do) It's got a frustrating "not fun" on-boarding. ie. the number of minutes from downloading "Julia" to getting cool satisfying results 1. It not a calcul…
It's looking like VS Code (julia-vscode.org) will be the equivalent, and it's gotten a good chunk of the way towards that - "plots docs repl" are all existing features and pretty easy to use. The docs [1] show a "Workspace" feature too. There's also some integration with tools like JET.jl [2] so that there's in-editor code analysis and diagnostics.
(And the extension works in VS Codium as well, so you can go completely FOSS if that's your wish.)
[1] https://www.julia-vscode.org/docs/stable/userguide/grid/ [2] https://aviatesk.github.io/JET.jl/stable/