Nested parallel computation (pmap + @distributed)

General Usage
1

I have N datasets (\text{data}^{}_1,..., \text{data}^{}_{10}). For each dataset I need to solve \hat{\theta}^{}_{d}=\arg\min\limits_\theta h(\theta;\text{data}^{}_{d}). I am using pmap to run the optimizations in parallel for each dataset in datas which is a struct which stores the ten datasets. The code looks something like this:

function compute_h(d,θ)
     Threads.@threads for i in eachindex(d)
           #do something to construct h(d,θ)
     end
     #return h(d,θ)
end

function find_argmin(d)
     # argmin = optimize(compute_h(d,θ), θ_guess)
     # return argmin
end

pool1 = [i for i=1:10]
pmap(d -> find_argmin(h(d)), WorkerPool(pool1), datas)

The above code runs without any problems. However, I get the error ERROR: No active worker available in pool when I try to parallelize compute_h over multiple processors instead of threading it (see below).

function compute_h(d,θ)
     @distributed for i in eachindex(d)
           #do something to construct h(d,θ)
     end
     #return h(d,θ)
end

My question: Is there anyway to nest a @distributed for loop within a pmap? My guess is that at a minimum I will need to specify that the @distributed for loop uses workers not already used for the pmap. However I have no idea how to specify a WorkerPool for the @distributed for loop.