Pkg.jl

..). You could setup a binary server for that and now the first time to plot is 0.4 seconds. Julia's arrays system is built so that most arrays that are used are not the simple Base.Array. Instead Julia has an AbstractArray interface definition (https://docs.julialang.org/en/v1/manual/interfaces/#man-inte...) which the Base.Array conforms to, and many effectively standard library packages like StaticArrays.jl, OffsetArrays.jl, etc. conform to, and thus they can be used in any other Julia package, like the differential equation solvers, solving nonlinear systems, optimization libraries, etc. There is a higher chance that packages depend on these packages then that they do not. They are only not part of the Julia distribution because the core idea is to move everything possible out to packages. There's not only a plan to make SuiteSparse and sparse matrix support be a package in 2.0, but also ideas about making the rest of linear algebra and arrays themselves into packages where Julia just defines memory buffer intrinsic (with likely the Arrays.jl package still shipped with the default image). At that point, are arrays not built into the language? I can understand using such a narrow definition for systems like Fortran or C where the standard library is essentially a fixed concept, but that just does not make sense with Julia. It's inherently fuzzy.

se to whatever compute resources will be requesting packages. For an airgapped solution, you could generate a static snapshot of some selection of the packages you want to serve, then serve them with nginx and point to that "static storage server" with the opensource Pkg Server, and it would all "just work". An example of how to generate a static storage server is here: https://github.com/JuliaPackaging/PkgServer.jl/blob/master/b..., you would serve the resultant directory structure with something like nginx, then point the Pkg Server to that server as the upstream storage server. I will note that Julia Computing offers an enterprise solution for dealing with secure/restrictive environments called JuliaTeam which provides this in a convenient, managed bundle, along with many other useful features. EDIT: Ah, I forgot to mention, Stefan and I gave a talk at JuliaCon 2020 that touches on some of this, here's a link to the timestamp of the relevant section: https://youtu.be/xPhnJCAkI4k?t=350 And for more info, here's the original planning issue (note some things have changed as we've implemented it over the last year, but the bones are the same): https://github.com/JuliaLang/Pkg.jl/issues/1377#issue-492482...