I have a similar situation. I am building up a team of modelers and would like to make sure we're all using the same versions of everything (Julia + packages). DeclarativePackages.jl looks promising/interesting and I'm curious what other things people have tried.
On Monday, November 16, 2015 at 12:21:31 PM UTC+8, David P. Sanders wrote: > > > > El domingo, 15 de noviembre de 2015, 21:05:45 (UTC-6), Sheehan Olver > escribió: >> >> >> I'm trying to figure out the "best" way to create a stable version of >> Julia + Gadfly + PyPlot + IJulia (+ other packages?) for a semester long >> course. I don't want to have the students run Pkg.add(...)/Pkg.update(), >> as packages have a tendency to occasionally break on updates, and it's a >> headache dealing with this during the lecture. >> >> Two possible solutions I can think of of are: >> >> 1) Prebake a .julia folder that contains all the necessary resources, >> with a script to reset in case the students break it with Pkg.update(). >> 2) Use system image >> >> http://docs.julialang.org/en/release-0.4/devdocs/sysimg/ >> >> that includes all the necessary packages. It's not really clear how to >> do this from the documentation, though. I'm also not sure how that would >> interact with Pkg.update() though, so probably instructions to delete >> .julia would also need to be given. >> >> >> Any other options I'm missing? If 2 is recommended, any tutorial how to >> do this? >> > > Another option is > > https://github.com/rened/DeclarativePackages.jl > > which allows you to run Julia with a given set of versions of given > packages. > > Possibly another option would be a Docker container image. >
