Using addprocs() and pmap() inside a function in Julia - parallel-processing

Question

In Julia, I want to use addprocs and pmap inside a function that is defined inside a module. Here's a silly example:
module test
using Distributions
export g, f
function g(a, b)
a + rand(Normal(0, b))
end
function f(A, b)
close = false
if length(procs()) == 1 # If there are already extra workers,
addprocs() # use them, otherwise, create your own.
close = true
end
W = pmap(x -> g(x, b), A)
if close == true
rmprocs(workers()) # Remove the workers you created.
end
return W
end
end
test.f(randn(5), 1)
This returns a very long error
WARNING: Module test not defined on process 4
WARNING: Module test not defined on process 3
fatal error on fatal error on WARNING: Module test not defined on process 2
43: : WARNING: Module test not defined on process 5
fatal error on fatal error on 5: 2: ERROR: UndefVarError: test not defined
in deserialize at serialize.jl:504
in handle_deserialize at serialize.jl:477
in deserialize at serialize.jl:696
...
in message_handler_loop at multi.jl:878
in process_tcp_streams at multi.jl:867
in anonymous at task.jl:63
Worker 3 terminated.
Worker 2 terminated.ERROR (unhandled task failure): EOFError: read end of file
WARNING: rmprocs: process 1 not removed
Worker 5 terminated.ERROR (unhandled task failure): EOFError: read end of file
4-element Array{Any,1}:Worker 4 terminated.ERROR (unhandled task failure): EOFError: read end of file
ERROR (unhandled task failure): EOFError: read end of file
ProcessExitedException()
ProcessExitedException()
ProcessExitedException()
ProcessExitedException()
What I'm trying to do is write a package that contains functions that perform operations that can be optionally parallelized at the user's discretion. So a function like f might take an argument par::Bool that does something like I've shown above if the user calls f with par = true and loops otherwise. So from within the definition of f (and within the definition of the module test), I want to create workers and broadcast the Distributions package and the function g to them.

Answer 1

What's wrong with using #everywhere in your function? The following, for example, works fine on my computer.
function f(A, b)
close = false
if length(procs()) == 1 # If there are already extra workers,
addprocs() # use them, otherwise, create your own.
#everywhere begin
using Distributions
function g(a, b)
a + rand(Normal(0, b))
end
end
close = true
end
W = pmap(x -> g(x, b), A)
if close == true
rmprocs(workers()) # Remove the workers you created.
end
return W
end
f(randn(5), 1)
Note: when I first ran this, I needed to recompile the Distributions package since it had been updated since I had last used it. When I first tried the above script right after the recompiling, it failed. But, I then quit Julia and reopened it and it worked fine. Perhaps that was what is causing your error?