The “using …” command is extremely slow: julia> @time using Plots 12.731850 seconds (17.25 M allocations: 976.326 MiB, 3.64% gc time) julia> @time using DifferentialEquations 34.780873 seconds (41.90 M allocations: 2.456 GiB, 2.63% gc time) julia> @time using Atom 3.137487 seconds (2.95 M all…

With a sysimage I managed to get the startup time to usuable speeds. I know that many people don’t care for startup time, but I can’t use the REPL, because I haven’t found any IDE workflow that is acceptable. Thank you.

[image] Volker_Weissmann: I know that many people don’t care for startup time, but I can’t use the REPL, because I haven’t found any IDE workflow that is acceptable. If you haven’t found an IDE you like, you can just use a Jupyter notebook as a REPL-like experience (but with persistence and m…

[image] stevengj: If you haven’t found an IDE you like, you can just use a Jupyter notebook as a REPL-like experience (but with persistence and multimedia)… I know about the jupyter notebook, but You cannot split your code into multiple files. Jupyter notebook can’t work with *.jl files, bu…

[image] Volker_Weissmann: You cannot split your code into multiple files. Yes you can, using NBInclude . (But if you are at the point of making lots of files, you should probably be creating modules and doing structured programming, and then calling your modules from your notebooks. In fact…

[image] Volker_Weissmann: Jupyter notebook can’t work with *.jl files, but all libraries are *.jl files Check jupytext , you can use a .jl file as a notebook. That is super usefull for version control too. Also in vscode you can have a similar interacting experience using the Shift + Enter wo…

[image] Volker_Weissmann: julia> @time using Plots 3.541552 seconds (5.94 M allocations: 426.191 MiB, 3.80% gc time, 18.98% compilation time) julia> @time using DifferentialEquations 6.540690 seconds (12.49 M allocations: 1.001 GiB, 6.18% gc time, 22.92% compilation time) Btw. a popular “m…

For tasks that are short pieces of simple code that run quickly in python, for example, Julia will probably never be faster the first time you do the operation. But for code that is numerically demanding, taking several minutes or more, Julia will very likely be faster, if the Julia code is written …

[image] tamasgal: Revise for example makes it mostly unnecessary to reload packages, in contrast to Python where you have to reload your complete session every time you change your code To be honest, I have to say that this differs dramatically from my experience, so maybe I’m doing something…

In Python, the issue is that anything you import gets not automatically updated when it changes. When you are executing scripts, this is not an issue. There is some IPython magic for auto-reloading imports, but I never got this working correctly in Jupyter. Pluto.jl allows redefinition of structs i…

Will Julia ever fix its "using ..." latency problems?

General Usage Performance

Tamas_Papp November 24, 2020, 8:25am 20

You asked pretty much the same thing a week ago, in a topic precisely about this, and got more or less the same answers.

Julia is developing very rapidly, but asking this every week is unlikely to result in new information.

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Will Julia ever fix its "using ..." latency problems?

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