New releases of julia are backward compatible with old. I have a bunch of Jupyter exercises in development version of Julia 0.4, 0.5 - Coursera et al
Is there an easy way to open Notebooks in the latest version for installed language, or an app to covert?
Menu item: Kernel, change kernel
Related
I have installed julia 1.6.2 , jupyter notebook and anaconda 3 according to this website:
https://datatofish.com/add-julia-to-jupyter/
I have also checked other websites and the steps of installation were similar. However, I have a problem in step 5 of the above link, as Julia 1.0 doesn't appear and only Python appears. ( Text File, Folder, and Terminal also appear in the Others section )
Any ideas on what the problem is? I have also set the environment variable of Julia by going to System Variables and adding the bin folder of julia to Path. ( Windows 10 OS)
Also, after installing Julia, these were the lines I entered to install other things.
import Pkg
# installs IJulia package
Pkg.add("IJulia")
using IJulia
# launch Jupyter notebook
notebook()
After these, I opened julia again and typed:
import Pkg
Pkg.add("NBInclude")
Julia 1.0 is really old, please use the latest 1.6.2 and follow official installation guide for Jupyter notebook support (IJulia): https://julialang.github.io/IJulia.jl/dev/manual/installation/
Usually, it's as simple as ]build IJulia, where pressing ] at the REPL drops you into the pkg> mode
I had this issue also. To solve it, you just need to rebuild your IJulia kernel by running command below on Julia apps:
Using Pkg
Pkg.add("IJulia")
Pkg.build("IJulia")
I agree with the answer of #jling. Use an up-to-date julia version if you can. In case you need an old julia version, I have recently created a docker container that provides julia 1.1 inside jupyter notebooks (and I think python as well).
You can find the Dockerfile here: https://github.com/cherrywoods/HorizontalCAS
Nevermind all the code, only the Dockerfile is relevant (and maybe the last section of the Readme). I guess this dockee fiöe should also work with julia 1.0 if you change the file accordingly.
What is the difference between Julia and Julia Pro offered by Julia Computing?
Does Julia Pro have any enterprise library which isn't available in Julia?
As you can read in the project description there are a few optional packages you can install on top of the "free version" (mostly in the area of Excel integration and business workflow), but the main "difference" is in the installation process, expecially in Windows or Mac:
With standard Julia you need three steps: install Julia itself, install an editor (e.g. Juno/Atom or VScode with the Julia extension), add the desired packages.
With JuliaPro, you have these three steps by just clicking an installer.
Julia Pro is a all in one simple solution as Anaconda for Python.
After installing either anaconda or jupyter, RStudio now shows some logs when installing packages, which indicate it's using R installed by anaconda, rather than the original version I had installed.
This is not a problem, until I try to install various packages, and I get some maze of frustrating c++ issues (due to graph dependencies).
What I've tried so far
Changed .libPaths() from the anaconda path to the default locations (but this doesn't help for R, only for R libraries)
Question
How can I tell which R installation RStudio is using, and how do I change it (from the anaconda version, back to the vanilla R version I previously had installed)
If you type version in the Rstudio console window it will tell you which version of R you are using...
> version
_
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
status
major 3
minor 6.3
year 2020
month 02
day 29
svn rev 77875
language R
version.string R version 3.6.3 (2020-02-29)
nickname Holding the Windsock
>
previous relaeases of R can be found here:
https://cran.r-project.org/bin/windows/base/old/
To those still searching for an answer and stumbled here
As listed above, basic info on the current R version is found entering this in the console of your RStudio
version
To view which R version is being used:
R.home() #tells you the R working directory
RSTUDIO_WHICH_R #tells you the source location of the R instance being used
Changing the version of R being used is much trickier. Consulting the official documentation can help, but realistically its easiest on Windows and a hassle on Mac/Linux. There might be some sneakier issues with Mac/Linux profiles and config variables, detailed more here
Best of luck!
When I run the simple profiling test case given in the Julia docs, my Julia kernel invariably dies regardless of environment. I've tried julia.exe, JuliaStudio, and Sublime Text with IJulia. I'm running a fresh install (via the standard 64-bit Windows installer) of version 0.3.0 on Windows 8.
Other components of Julia appear to be working without a hitch. What might be the issue?
This is a known bug in Julia 0.3.0, and will be fixed in the upcoming Julia 0.3.1
I'm planning a new build system for our project running on Centos 5.4. I intend to use SCons. I noticed the latest stable SCons version is 2.0.1 while Centos 5.4 comes with 1.2.
I'm new to SCons so I'd like to understand more about the features/stability ratio between the versions.
Would you recommend installing and using the latest version or sticking to what comes from my OS repository?
Since 1.2 the changes have been bug fixes and documentation improvements. There haven't been any new features.
The 2.x version of SCons drops support for Python versions older than 2.4, updating some of the internal code to use newer idioms but without affecting any user visible APIs. That's the reason for the major version number change. If CentOS comes with a recent version of python then this won't affect you either way.
There have been a lot of fixes for newer versions of the various Microsoft compiler versions, but this won't affect a CentOS install.
The bug fixes since SCons 1.2.0 also solve problems in the Fortran, TeX and LaTeX builders. If you make use of Fortran or LaTeX then it would probably be worth upgrading. Otherwise I think you would be hard pushed to spot any day-to-day difference between 1.2.0 and 2.0.1.