I need help getting a function to run in parallel. I want to distribute both data and packages to all workers, see the following example (which doesn’t work):

```
#Simulated data (n observations, p variables, tr truevariables, sig error)
n, p, q, tr_p1, tr_p2, sig = 1000, 5000, 2, 10, 5, 0.5
X = randn(n, p)
Y = randn(n, q)
B_true1 = [randn(tr_p1)..., zeros(p-tr_p1)...]
B_true2 = [randn(tr_p2)..., zeros(p-tr_p2)...]
y1 = X*B_true1 + sig*randn(n)
y2 = X*B_true2 + sig*randn(n)
Y[:,1] = y1
Y[:,2] = y2
#Initialize 4 workers
using Distributed
addprocs(4)
@everywhere using StructuredOptimization, Random
@everywhere function l1sep(λ1,λ2) # opt function with random permutation of train and test data
cvind = randperm(size(X)[1])
Ytrain = Y[cvind[1:800],:]
Ytest = Y[cvind[801:size(X)[1]],:]
Xtrain = X[cvind[1:800],:]
Xtest= X[cvind[801:size(X)[1]],:]
B = Variable(size(X)[2],size(Y)[2])
@minimize ls(Xtrain*B - Ytrain) + λ1*norm(B[:,1],1) + λ2*norm(B[:,2],1) with ZeroFPR()
Bhat = copy(~B)
Ytestpred = Xtest*Bhat
MSEtest = (0.5*norm(Ytestpred-Ytest,2)^2)/(length(Ytest)*size(X)[2])
return MSEtest
end
# Trying to run 4 optimizations in parallel doesn't work
XY = @spawn X,Y
pmap(l1sep(5.0,5.0),XY)
# How to collect MSEtest from the different workers?
```