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Makeitso.jl

Make-like behavior to facilitate writing long-running scripts. The idea is that the user supplies the dependencies between targets and recipes for how to make a target from those dependencies. If changes happen to one recipe (cf. if one edits one source file in a make project), only affected variables will be recomputed.

Upon computation, a backup is written to disk. This means that even when the work on the script is resumed in a different session (because you went home, julia crashed, you ran out of memory, you share the data with coworkers over e.g. Dropbox), only missing and out-of-date targets will be recomputed.

This saves a lot of time and unnecessary reruns of computations. It also relieves the programmer of having to track all dependencies and keep a clear picture of the workspace in their head.

Files for backups are based of the hash of both the target recipe and the parameter values at which the build is requested. This allows for multiple versions to coexist on disk, leading to a further reduction of the number of computed variables.

Makeitso suppports the computation of atomic targets and of sweeps over the Cartesian product of provided variable ranges. The return value of such a @sweep should always be a named tuple. Upon building, sweeps return a DataFrame with one row for each value of the set of variables that were swept over. The columns of the DataFrame will correspond to the fields of the returned named tuple. In addition, the parameter values used for each iteration of the sweep will be included as well. The following names currently cannot be used as either parameter names or field names in the returned value of the sweep recipe: [timestamp, hash, tree_hash, path, params].

Example

Consider the script in file DrWatson.projectdir("sweep4.jl")

using Makeitso

@target base () -> 10

module Mod
    function f(x)
        return x + 1
    end

    function square_root(x)
        return sqrt(x)
    end
end

@target ore (;seed, p) -> begin
    y= Mod.f(seed) + p
    return y
end

@sweep solutions (base, !ore, ;seed = seed, p) -> begin
    return (;sol = Mod.square_root(ore) + base)
end

@target average (solutions,;seed, p) -> begin
    sum(solutions.sol)
end

x = make(average; seed=[1.0,2.0,3.0], p=3.14)
y = make(solutions; seed=[1.0,2.0,3.0], p=3.14)

z1 = sweep(average; seed=Ref([1.0,2.0,3.0]), p=[2.78, 3.14])
z2 = sweep(average; seed=[[1.0,2.0,3.0], [4.0,5.0,6.0]], p=[2.78, 3.14])

The toplevel @target is average, which computes the sum of all entries in the column sol of the DataFrame computed by the @sweep solutions. The @sweep solutions has two dependencies: one named base which is shared among all iterations (which should and will only be computed once), and another named ore which depends on the swept over variable seed. Iteration specific dependencies are declared by an exclamation mark. Important: Currently, all shared dependencies must preceed all iteration specific dependencies in the dependency declaration list.

The last two lines ask for average to be built w.r.t. a range of parameters. This could have been achieved by defining another @sweep, but because this scenario is so common, it is supported in this lightweight format as well. The sweep function used a heuristic to determine which of the keyword arguments are to be considered atomic parameters, and which to be considered variable ranges. Iterable containers meant as parameters should be protected by boxing them in a Ref.

Running this script the first time in a new Julia session results in

[ Info: [0] making average at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [1] making solutions at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [2] making base at NamedTuple():
[ Info: [2] target base at NamedTuple(): computing from deps!
[ Info: [2] making iteration solutions at (seed = 1.0,):
[ Info: [3] making ore at (p = 3.14, seed = 1.0):
[ Info: [3] target ore at (p = 3.14, seed = 1.0): computing from deps!
[ Info: [2] iteration solutions at (p = 3.14, seed = 1.0): computing from deps!
[ Info: [2] making iteration solutions at (seed = 2.0,):
[ Info: [3] making ore at (p = 3.14, seed = 2.0):
[ Info: [3] target ore at (p = 3.14, seed = 2.0): computing from deps!
[ Info: [2] iteration solutions at (p = 3.14, seed = 2.0): computing from deps!
[ Info: [2] making iteration solutions at (seed = 3.0,):
[ Info: [3] making ore at (p = 3.14, seed = 3.0):
[ Info: [3] target ore at (p = 3.14, seed = 3.0): computing from deps!
[ Info: [2] iteration solutions at (p = 3.14, seed = 3.0): computing from deps!
[ Info: [1] sweep solutions at (p = 3.14, seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\solutions.1YvXce9RGRZ.dir for result files.
[ Info: Added 3 entries.
[ Info: [0] target average at (p = 3.14, seed = [1.0, 2.0, 3.0]): computing from deps!
[ Info: [0] making solutions at (seed = [1.0, 2.0, 3.0], p = 3.14):
[ Info: [0] sweep solutions at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from cache.
[ Info: [0] making average.sweep at (p = [2.78, 3.14], seed = [1.0, 2.0, 3.0]):
[ Info: [1] making iteration average.sweep at (p = 2.78,):
[ Info: [2] making average at (p = 2.78, seed = [1.0, 2.0, 3.0]):
[ Info: [3] making solutions at (p = 2.78, seed = [1.0, 2.0, 3.0]):
[ Info: [4] making base at NamedTuple():
[ Info: [4] target base at NamedTuple() retrieved from cache.
[ Info: [4] making iteration solutions at (seed = 1.0,):
[ Info: [5] making ore at (p = 2.78, seed = 1.0):
[ Info: [5] target ore at (p = 2.78, seed = 1.0): computing from deps!
[ Info: [4] iteration solutions at (p = 2.78, seed = 1.0): computing from deps!
[ Info: [4] making iteration solutions at (seed = 2.0,):
[ Info: [5] making ore at (p = 2.78, seed = 2.0):
[ Info: [5] target ore at (p = 2.78, seed = 2.0): computing from deps!
[ Info: [4] iteration solutions at (p = 2.78, seed = 2.0): computing from deps!
[ Info: [4] making iteration solutions at (seed = 3.0,):
[ Info: [5] making ore at (p = 2.78, seed = 3.0):
[ Info: [5] target ore at (p = 2.78, seed = 3.0): computing from deps!
[ Info: [4] iteration solutions at (p = 2.78, seed = 3.0): computing from deps!
[ Info: [3] sweep solutions at (p = 2.78, seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\solutions.1YvXce9RGRZ.dir for result files.
[ Info: Added 6 entries.
[ Info: [2] target average at (p = 2.78, seed = [1.0, 2.0, 3.0]): computing from deps!
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]): computing from deps!
[ Info: [1] making iteration average.sweep at (p = 3.14,):
[ Info: [2] making average at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [2] target average at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]): computing from deps!
[ Info: [0] sweep average.sweep at (p = [2.78, 3.14], seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\average.sweep.6Lh9bApq4vd.dir for result files.
[ Info: Added 2 entries.
[ Info: [0] making average.sweep at (p = [2.78, 3.14], seed = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]):
[ Info: [1] making iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]):
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 2.78, seed = [4.0, 5.0, 6.0]):
[ Info: [2] making average at (p = 2.78, seed = [4.0, 5.0, 6.0]):
[ Info: [3] making solutions at (p = 2.78, seed = [4.0, 5.0, 6.0]):
[ Info: [4] making base at NamedTuple():
[ Info: [4] target base at NamedTuple() retrieved from cache.
[ Info: [4] making iteration solutions at (seed = 4.0,):
[ Info: [5] making ore at (p = 2.78, seed = 4.0):
[ Info: [5] target ore at (p = 2.78, seed = 4.0): computing from deps!
[ Info: [4] iteration solutions at (p = 2.78, seed = 4.0): computing from deps!
[ Info: [4] making iteration solutions at (seed = 5.0,):
[ Info: [5] making ore at (p = 2.78, seed = 5.0):
[ Info: [5] target ore at (p = 2.78, seed = 5.0): computing from deps!
[ Info: [4] iteration solutions at (p = 2.78, seed = 5.0): computing from deps!
[ Info: [4] making iteration solutions at (seed = 6.0,):
[ Info: [5] making ore at (p = 2.78, seed = 6.0):
[ Info: [5] target ore at (p = 2.78, seed = 6.0): computing from deps!
[ Info: [4] iteration solutions at (p = 2.78, seed = 6.0): computing from deps!
[ Info: [3] sweep solutions at (p = 2.78, seed = [4.0, 5.0, 6.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\solutions.1YvXce9RGRZ.dir for result files.
[ Info: Added 9 entries.
[ Info: [2] target average at (p = 2.78, seed = [4.0, 5.0, 6.0]): computing from deps!
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [4.0, 5.0, 6.0]): computing from deps!
[ Info: [1] making iteration average.sweep at (p = 3.14, seed = [4.0, 5.0, 6.0]):
[ Info: [2] making average at (p = 3.14, seed = [4.0, 5.0, 6.0]):
[ Info: [3] making solutions at (p = 3.14, seed = [4.0, 5.0, 6.0]):
[ Info: [4] making base at NamedTuple():
[ Info: [4] target base at NamedTuple() retrieved from cache.
[ Info: [4] making iteration solutions at (seed = 4.0,):
[ Info: [5] making ore at (p = 3.14, seed = 4.0):
[ Info: [5] target ore at (p = 3.14, seed = 4.0): computing from deps!
[ Info: [4] iteration solutions at (p = 3.14, seed = 4.0): computing from deps!
[ Info: [4] making iteration solutions at (seed = 5.0,):
[ Info: [5] making ore at (p = 3.14, seed = 5.0):
[ Info: [5] target ore at (p = 3.14, seed = 5.0): computing from deps!
[ Info: [4] iteration solutions at (p = 3.14, seed = 5.0): computing from deps!
[ Info: [4] making iteration solutions at (seed = 6.0,):
[ Info: [5] making ore at (p = 3.14, seed = 6.0):
[ Info: [5] target ore at (p = 3.14, seed = 6.0): computing from deps!
[ Info: [4] iteration solutions at (p = 3.14, seed = 6.0): computing from deps!
[ Info: [3] sweep solutions at (p = 3.14, seed = [4.0, 5.0, 6.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\solutions.1YvXce9RGRZ.dir for result files.
[ Info: Added 12 entries.
[ Info: [2] target average at (p = 3.14, seed = [4.0, 5.0, 6.0]): computing from deps!
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [4.0, 5.0, 6.0]): computing from deps!
[ Info: [0] sweep average.sweep at (p = [2.78, 3.14], seed = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\average.sweep.6Lh9bApq4vd.dir for result files.
[ Info: Added 4 entries.
4×3 DataFrame
 Row │ average   seed             p        
     │ Float64?  Array…?          Float64?
─────┼─────────────────────────────────────
   1 │  38.8797  [4.0, 5.0, 6.0]      2.78
   2 │  37.1943  [1.0, 2.0, 3.0]      2.78
   3 │  39.0606  [4.0, 5.0, 6.0]      3.14
   4 │  37.4171  [1.0, 2.0, 3.0]      3.14

This output is rather verbose, but the important part are the lines containing !!!, indicating acutal computations. Asking to make target average results in 9 computations: 1 copy of base, 3 of ore (for the 3 values of seed), 3 where base and ore are processed in the iterations for sweep solution, 1 for solution itself, which collects the information computed in its iterations, and finally one for the toplevel target average.

Note that when explicitly making the sweep solutions, no new computations are triggered, since all required results are computed already by making average.

Upon completion the contents of DrWatson.datadir() will look like this:

└───sweep5
    ├───average.63678zPY2rK.dir
    │       p=2.78.1nF5HKz03Py.jld2
    │       p=2.78.5hPMAUVKJFb.jld2
    │       p=3.14.6blorQZib97.jld2
    │       p=3.14.CoXCiMub8Ro.jld2
    │
    ├───average.sweep.6Lh9bApq4vd.dir
    │       p=2.78.7OlQWfYVHko.jld2
    │       p=2.78.J7GFC87U3Fh.jld2
    │       p=3.14.4fg2RrLKQ4G.jld2
    │       p=3.14.824AT3Qr5iR.jld2
    │
    ├───base.BrKrFd0kr39.dir
    │       9hXKeOK7BYa.jld2
    │
    ├───ore.CrdBumEWCwZ.dir
    │       p=2.78_seed=1.0.3IA3pEDIKAh.jld2
    │       p=2.78_seed=2.0.3ZOzuLvcGIY.jld2
    │       p=2.78_seed=3.0.8FHnvLU9m86.jld2
    │       p=2.78_seed=4.0.8KpxOdrOhEH.jld2
    │       p=2.78_seed=5.0.4DH70LVr2lx.jld2
    │       p=2.78_seed=6.0.HwIz9PLreBb.jld2
    │       p=3.14_seed=1.0.zNZMKtM5eu.jld2
    │       p=3.14_seed=2.0.H4R2N6UhyO.jld2
    │       p=3.14_seed=3.0.8TEnXyWUMx2.jld2
    │       p=3.14_seed=4.0.4XHk9VI5czL.jld2
    │       p=3.14_seed=5.0.KFCTtJy5XEo.jld2
    │       p=3.14_seed=6.0.9YzboOuffEc.jld2
    │
    └───solutions.1YvXce9RGRZ.dir
            p=2.78_seed=1.0.3KQV8mN5Rhl.jld2
            p=2.78_seed=2.0.5pavstnysqS.jld2
            p=2.78_seed=3.0.3xBcrncVwDs.jld2
            p=2.78_seed=4.0.4l907SzgE7T.jld2
            p=2.78_seed=5.0.BcRxkq47v75.jld2
            p=2.78_seed=6.0.3Hd7aI1DeWL.jld2
            p=3.14_seed=1.0.4a4MWlZncdu.jld2
            p=3.14_seed=2.0.K7yRD6aDT1W.jld2
            p=3.14_seed=3.0.5ykZTSxVHcI.jld2
            p=3.14_seed=4.0.EWXe5G4FB3Z.jld2
            p=3.14_seed=5.0.230IfUT4MQy.jld2
            p=3.14_seed=6.0.FoXrrIT8wAW.jld2

A second run of the scripts results in:

WARNING: replacing module Mod.
[ Info: [0] making average at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [0] target average at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [0] making solutions at (seed = [1.0, 2.0, 3.0], p = 3.14):
[ Info: [1] making base at NamedTuple():
[ Info: [1] target base at NamedTuple() retrieved from cache.
[ Info: [1] making iteration solutions at (seed = 1.0,):
[ Info: [1] iteration solutions at (seed = 1.0,) retrieved from disk.
[ Info: [1] making iteration solutions at (seed = 2.0,):
[ Info: [1] iteration solutions at (seed = 2.0,) retrieved from disk.
[ Info: [1] making iteration solutions at (seed = 3.0,):
[ Info: [1] iteration solutions at (seed = 3.0,) retrieved from disk.
[ Info: [0] sweep solutions at (p = 3.14, seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\solutions.1YvXce9RGRZ.dir for result files.
[ Info: Added 12 entries.
[ Info: [0] making average.sweep at (p = [2.78, 3.14], seed = [1.0, 2.0, 3.0]):
[ Info: [1] making iteration average.sweep at (p = 2.78,):
[ Info: [1] iteration average.sweep at (p = 2.78,) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14,):
[ Info: [1] iteration average.sweep at (p = 3.14,) retrieved from disk.
[ Info: [0] sweep average.sweep at (p = [2.78, 3.14], seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\average.sweep.6Lh9bApq4vd.dir for result files.
[ Info: Added 4 entries.
[ Info: [0] making average.sweep at (p = [2.78, 3.14], seed = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]):
[ Info: [1] making iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]):
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 2.78, seed = [4.0, 5.0, 6.0]):
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [4.0, 5.0, 6.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14, seed = [4.0, 5.0, 6.0]):
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [4.0, 5.0, 6.0]) retrieved from disk.
[ Info: [0] sweep average.sweep at (p = [2.78, 3.14], seed = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\average.sweep.6Lh9bApq4vd.dir for result files.
[ Info: Added 4 entries.
4×3 DataFrame
 Row │ average   seed             p        
     │ Float64?  Array…?          Float64? 
─────┼─────────────────────────────────────
   1 │  38.8797  [4.0, 5.0, 6.0]      2.78
   2 │  37.1943  [1.0, 2.0, 3.0]      2.78
   3 │  39.0606  [4.0, 5.0, 6.0]      3.14
   4 │  37.4171  [1.0, 2.0, 3.0]      3.14

This time around, no computations were performed at all!

Let's kill julia and run the file again:

[ Info: [0] making average at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [0] target average at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [0] making solutions at (seed = [1.0, 2.0, 3.0], p = 3.14):
[ Info: [1] making base at NamedTuple():
[ Info: [1] target base at NamedTuple() retrieved from disk.
[ Info: [1] making iteration solutions at (seed = 1.0,):
[ Info: [1] iteration solutions at (seed = 1.0,) retrieved from disk.
[ Info: [1] making iteration solutions at (seed = 2.0,):
[ Info: [1] iteration solutions at (seed = 2.0,) retrieved from disk.
[ Info: [1] making iteration solutions at (seed = 3.0,):
[ Info: [1] iteration solutions at (seed = 3.0,) retrieved from disk.
[ Info: [0] sweep solutions at (p = 3.14, seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\solutions.1YvXce9RGRZ.dir for result files.
[ Info: Added 12 entries.
[ Info: [0] making average.sweep at (p = [2.78, 3.14], seed = [1.0, 2.0, 3.0]):
[ Info: [1] making iteration average.sweep at (p = 2.78,):
[ Info: [1] iteration average.sweep at (p = 2.78,) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14,):
[ Info: [1] iteration average.sweep at (p = 3.14,) retrieved from disk.
[ Info: [0] sweep average.sweep at (p = [2.78, 3.14], seed = [1.0, 2.0, 3.0]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\average.sweep.6Lh9bApq4vd.dir for result files.
[ Info: Added 4 entries.
[ Info: [0] making average.sweep at (p = [2.78, 3.14], seed = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]):
[ Info: [1] making iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]):
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]):
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [1.0, 2.0, 3.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 2.78, seed = [4.0, 5.0, 6.0]):
[ Info: [1] iteration average.sweep at (p = 2.78, seed = [4.0, 5.0, 6.0]) retrieved from disk.
[ Info: [1] making iteration average.sweep at (p = 3.14, seed = [4.0, 5.0, 6.0]):
[ Info: [1] iteration average.sweep at (p = 3.14, seed = [4.0, 5.0, 6.0]) retrieved from disk.
[ Info: [0] sweep average.sweep at (p = [2.78, 3.14], seed = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]): collect iterations.
[ Info: Scanning folder c:\Users\krcools\.julia\dev\Makeitso\data\examples\sweep5\average.sweep.6Lh9bApq4vd.dir for result files.
[ Info: Added 4 entries.
4×3 DataFrame
 Row │ average   seed             p        
     │ Float64?  Array…?          Float64?
─────┼─────────────────────────────────────
   1 │  38.8797  [4.0, 5.0, 6.0]      2.78
   2 │  37.1943  [1.0, 2.0, 3.0]      2.78
   3 │  39.0606  [4.0, 5.0, 6.0]      3.14
   4 │  37.4171  [1.0, 2.0, 3.0]      3.14

Now, all information has to be loaded from disk, but no new computations were required, since these have been done in the previous session.

Notes

  • The @target macro creates a variable A etc which has type Target and should not be used to hold the result of building target A. This is by design to discourage creating untracked variables. Similarly, @sweep S creates a variable S of type Sweep.
  • Recipes resulting in nothing are not valid as nothing indicates absence of an in-memory cached value.
  • The correct way to supply parameters is to use keywords. For example make(D; p=314) will propagate this keyword to all recipes of D and its dependencies. See examples/hello_kw.jl for details.
  • The recipe supplied to @target and @sweep are hashed and this hash is used to track any changes done to the code, which would invalidate the computed values and all values that depend on them. This system can of course be fooled easily by having the recipes rely on untracked code. Perhaps in the future a pedantic mode can be introduced forcing the user the work in a clean git directory with a manifest containing only versioned dependencies.
  • For savename generation and the collection of results files to build the DataFrame computed by @sweep, the package relies on DrWatson.
  • @target memonly=true A () -> 3 creates a target that does not write backups to disk. This will in general results in more computations, but it will avoid (especially in sweeps) that the disk is flooded by large intermediary results.
  • A space is required between the target name and the parentheses containing the dependencies. In other words, the macro takes two arguments (or more if options are supplied)

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Make-like behaviour for Julia scripts

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v2.2.0 Latest
Nov 4, 2025
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