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Can someone comment on the ease of distributing Julia code? Can I easily take a bit of code I write (w/o external libraries) and produce a single binary I can s

Can someone comment on the ease of distributing Julia code? Can I easily take a bit of code I write (w/o external libraries) and produce a single binary I can ship to someone or do they require a full Julia environment to run it?

jakobnissen 6y ago
They require the full Julia environment to run it - and it's a heavy environment. It is possible to compile a binary that includes the environment and the compiler, but IIRC, that will result in a >400 MB hello-world script taking 150 MB of RAM to run. The core devs have mentioned they are going to add the capacity to compile to actual static binaries, but that does not seem to be a top priority, so I wouldn't hold my breadth waiting for it.
- adgjlsfhk1 6y ago
  Note that this feature doesn't necessary require work by the core devs. It is completely feasible (at least in theory) to write a library that can output static exectuables.
  - ChrisRackauckas 6y ago
    It's actually done all of the time. GPUCompiler.jl, the core of the CUDA and AMD GPU stack, builds static binaries (compiled to .ptx by LLVM for CUDA for example, but the choice is just a switch), then stashes those binaries to use with a ccall in a Julia function. You could in theory use that stack to statically-compile anything that's GPU compliable, and it's really well-tested.
eigenspace 6y ago
Doing this without the user having Julia installed would require using PackageCompiler.jl to make a standalone executable. https://julialang.github.io/PackageCompiler.jl/dev/apps/ https://julialang.github.io/PackageCompiler.jl/dev/apps/ This is a pretty stable well established process now, but the binaries it produces are huge because they actually have the full Julia runtime in them. Active work is happening on small binary static compilation. We already do it for GPUs, we just have to repurpose our GPU AOT compilation pipeline for the CPU. There are proofs of concept that currently work, but something more polished is feeling like it’ll probably be another year or so.

jakobnissen 6y ago

They require the full Julia environment to run it - and it's a heavy environment. It is possible to compile a binary that includes the environment and the compiler, but IIRC, that will result in a >400 MB hello-world script taking 150 MB of RAM to run. The core devs have mentioned they are going to add the capacity to compile to actual static binaries, but that does not seem to be a top priority, so I wouldn't hold my breadth waiting for it.

adgjlsfhk1 6y ago
Note that this feature doesn't necessary require work by the core devs. It is completely feasible (at least in theory) to write a library that can output static exectuables.
- ChrisRackauckas 6y ago
  It's actually done all of the time. GPUCompiler.jl, the core of the CUDA and AMD GPU stack, builds static binaries (compiled to .ptx by LLVM for CUDA for example, but the choice is just a switch), then stashes those binaries to use with a ccall in a Julia function. You could in theory use that stack to statically-compile anything that's GPU compliable, and it's really well-tested.

eigenspace 6y ago

Doing this without the user having Julia installed would require using PackageCompiler.jl to make a standalone executable. https://julialang.github.io/PackageCompiler.jl/dev/apps/ https://julialang.github.io/PackageCompiler.jl/dev/apps/ This is a pretty stable well established process now, but the binaries it produces are huge because they actually have the full Julia runtime in them. Active work is happening on small binary static compilation. We already do it for GPUs, we just have to repurpose our GPU AOT compilation pipeline for the CPU. There are proofs of concept that currently work, but something more polished is feeling like it’ll probably be another year or so.