With an "app" we here mean a "bundle" of files where one of these files is an executable and where this bundle can be sent to another machine while still allowing the executable to run.

Use-cases for Julia-apps are for example when one wants to provide some kind of functionality where the fact that the code was written in Julia is just an implementation detail and where requiring the user to download and use Julia to run the code would be a distraction. There is also no need to provide the original Julia source code for apps since everything gets baked into the sysimage.

Creating an app

The source of an app is a package with a project and manifest file. You can easily create this using the package manager:

using Pkg

In the code for the package, there should be a function with the signature

function julia_main()::Cint
  # do something based on ARGS?
  return 0 # if things finished successfully

defined (you can customize this), which will be the entry point of the app (the function that runs when the executable in the app is run). A skeleton of an app to start working from can be found here.

The app is then compiled using the create_app function that takes a path to the source code of the app and the destination where the app should be compiled to. This will bundle all required libraries for the app to run on another machine where the same Julia that created the app can run. As an example, in the code snippet below, the example app linked above is compiled and run:

❯ julia -q --project

julia> using PackageCompiler

julia> create_app("MyApp", "MyAppCompiled")
[ Info: PackageCompiler: creating base system image (incremental=false), this might take a while...
[ Info: PackageCompiler: creating system image object file, this might take a while...

julia> exit()

❯ MyAppCompiled/bin/MyApp foo bar --julia-args -t4
ARGS = ["foo", "bar"]
Base.PROGRAM_FILE = "./bin/MyApp"
DEPOT_PATH = ["/home/kc/JuliaPkgs/PackageCompiler.jl/MyAppCompiled/share/julia"]
Running the artifact
The result of 2*5^2 - 10 == 40.000000

Note that the arguments passed to the executable are available in the global variable ARGS. Standard julia arguments (like how many threads should be used) are passed in after the --julia-args argument.

The resulting executable is found in the bin folder in the compiled app directory. The compiled app directory MyAppCompiled could now be put into an archive and sent to another machine or an installer could be wrapped around the directory, perhaps providing a better user experience than just an archive of files.

Compilation of functions

In the same way as files for precompilation could be given when creating sysimages, the same keyword arguments are used to add precompilation to apps.

Incremental vs non-incremental sysimage

In the section about creating sysimages, there was a short discussion about incremental vs non-incremental sysimages. In short, an incremental sysimage is built on top of another sysimage, while a non-incremental is created from scratch. For sysimages, it makes sense to use an incremental sysimage built on top of Julia's default sysimage since we wanted the benefit of having a responsive REPL that it provides. For apps, this is no longer the case, the sysimage is not meant to be used when working interactively, it only needs to be specialized for the specific app. Therefore, by default, incremental=false is used for create_app. If, for some reason, one wants an incremental sysimage, incremental=true could be passed to create_app. With the example app, a non-incremental sysimage is about 70MB smaller than the default sysimage.

Filtering stdlibs

By default, all standard libraries are included in the sysimage. It is possible to only include those standard libraries that the project needs. This is done by passing the keyword argument filter_stdlibs=true to create_app. This causes the sysimage to be smaller, and possibly load faster. The reason this is not the default is that it is possible to "accidentally" depend on a standard library without it being reflected in the Project file. For example, it is possible to call rand() from a package without depending on Random, even though that is where the method is defined. If Random was excluded from the sysimage that call would then error. As another example, matrix multiplication, rand(3,3) * rand(3,3) requires both the standard libraries LinearAlgebra and Random This is because these standard libraries practice "type-piracy", just loading those packages can cause code to change behavior.

Nevertheless, the option is there to use. Just make sure to properly test the app with the resulting sysimage.

Multiple executables

By default, the binary in the bin directory takes the name of the project, as defined in Project.toml and the julia function that will be called when running it is julia_main. You can change the executable name and the julia function using the executables keyword argument to create_app. As an example, if you want to have two executables called A and B calling the julia functions main_A and main_B, respectively, you would pass executables= ["A" => "main_A", "B" => "main_B"]. Note that main_A and main_B both need to not take any arguments and be annotated to return a Cint, for example:

function main_A()::Cint


Since we want to send the app to other machines the app we create must be "relocatable". With an app being relocatable we mean it does not rely on specifics of the machine where the app was created. Relocatability is not an absolute measure, most apps assume some properties of the machine they will run on, like what operating system is installed and the presence of graphics drivers if one wants to show graphics. On the other hand, embedding things into the app that is most likely unique to the machine, such as absolute paths to libraries, means that the application almost surely will not run properly on another machine.

For something to be relocatable, everything that it depends on must also be relocatable. In the case of an app, the app itself and all the Julia packages it depends on must also relocatable. This is a bit of an issue because the Julia package ecosystem has rarely given much thought to relocatability since creating "apps" has not been common.

The main problem with relocatability of Julia packages is that many packages are encoding fundamentally non-relocatable information into the source code. As an example, many packages tend to use a build.jl file (which runs when the package is first installed) that looks something like:

lib_path = find_library("libfoo")
write("deps.jl", "const LIBFOO_PATH = $(repr(lib_path))")

The main package file then contains:

module Package

if !isfile("../build/deps.jl")
    error("run\"Package\") to re-build Package")
function __init__()
    libfoo = Libdl.dlopen(LIBFOO_PATH)


end # module

The absolute path to lib_path that find_library found is thus effectively included into the source code of the package. Arguably, the whole build system in Julia is inherently non-relocatable because it runs when the package is being installed which is a concept that does not make sense when distributing an app.

Some packages do need to call into external libraries and use external binaries so the question then arises: "how are these packages supposed to do this in a relocatable way?" The answer is to use the "artifact system" introduced in Julia 1.3, and described in the following blog post. The artifact system is a declarative way of downloading and using "external files" like binaries and libraries. How this is used in practice is described later. Another useful tool is the Julia package RelocatableFolders.jl.


The way to depend on external libraries or binaries when creating apps is by using the artifact system. PackageCompiler will bundle all artifacts needed by the project, and set up things so that they can be found during runtime on other machines.

The example app uses the artifact system to depend on a very simple toy binary that does some simple arithmetic. It is instructive to see how the artifact file is used in the source.


Compile-time preferences used by any of the packages included in the app will be stored in the sysimage. To support runtime preferences, all preferences that the app "sees" during the compilation process are stored in the app bundle under <app_dir>/share/julia/LocalPreferences.toml. Note that preferences loaded at compile time are not affected by the values in the LocalPreferences.toml, but modifying the file will change the value of preferences loaded at runtime.

To learn more about compile time preferences and runtime preferences, please refer to the Preferences.jl docs.

Reverse engineering the compiled app

While the created app is relocatable and no source code is bundled with it, there are still some things about the build machine and the source code that can be "reverse engineered".

Absolute paths of the build machine

Julia records the paths and line-numbers for methods when they are getting compiled. These get cached into the sysimage and can be found e.g. by dumping all strings in the sysimage:

❯ strings | grep MyApp

This is a problem that the Julia standard libraries themselves have:

julia> @which rand()
rand() in Random at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/1.7/Random/src/Random.jl:256

Using reflection and finding lowered code

There is nothing preventing someone from starting Julia with the sysimage that comes with the app. In fact, to support distributed computing that needs to start up new Julia processes, there is also a julia executable in the bin folder that uses the custom sysimage. While the source code is not available one can read the "lowered code" and use reflection to find things like the name of fields in structs and global variables etc:

❯ ./julia -q

julia> names(MyApp; all=true)
18-element Vector{Symbol}:

julia> @code_lowered MyApp.real_main()
1 ─        Core.NewvarNode(:(#2))
│          Core.NewvarNode(:(n))
│   %3   = MyApp.ARGS
│          value@_17 = %3
│   %5   = Base.repr(%3)
│          Base.println("ARGS = ", %5)
│          value@_17
│   %8   = Base.PROGRAM_FILE
│          value@_16 = %8
│   %10  = Base.repr(%8)

Preferences in <app_dir>/share/julia

As described above, a TOML file with all preferences active during the compilation process will be stored with the app bundle. If your preferences may contain confidential information, you can either delete the <app_dir>/share/julia/LocalPreferences.toml file before distributing the app bundle, or suppress the preference file generation by passing include_preferences=false to create_app. Note, however, that if the preference file is not present, any preference loaded in your app at runtime will use their default value (or crash, if no default is provided).