# Getting Reproducible Results in Distributed Computing with Randomness

**URL:** https://discourse.julialang.org/t/getting-reproducible-results-in-distributed-computing-with-randomness/103589
**Category:** General Usage
**Tags:** question, distributed, random
**Created:** [September 6, 2023, 9:31pm UTC](https://discourse.julialang.org/t/getting-reproducible-results-in-distributed-computing-with-randomness/103589 "2023-09-06T21:31:08Z")
**Posts on this page:** 3
**Page:** 1

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### Author: ![naonagas](https://avatars.discourse-cdn.com/v4/letter/n/ccd318/32.png) [@naonagas](https://discourse.julialang.org/u/naonagas)
#### Post date: [September 6, 2023, 9:31pm UTC](https://discourse.julialang.org/t/getting-reproducible-results-in-distributed-computing-with-randomness/103589/1 "2023-09-06T21:31:08Z")

</div>

I’m stuck on how to set different random seeds for each worker in distributed computation.

```julia
using Distributed, SharedArrays

# Start Julia with multiple workers
addprocs()

@everywhere using Random

# Function to set a unique random seed for each worker
@everywhere function set_worker_seed()
    worker_id = myid() - 1 # Worker IDs start from 2, so we subtract 1
    seed = 42 + 1000*worker_id # Adjust the base seed value as needed
    Random.seed!(seed)
    println("Worker $worker_id seed: $seed")
end

# Set a unique random seed for each worker
for p in workers()
    @spawnat p set_worker_seed()
end

v = SharedArray{Float64}(10)
# Define a function that uses the random number generator
@sync @distributed for i=1:10
    v[i] = rand()
end
println(v)

```

My hope is to set different random seeds for each worker (so that there is no overlap in the value of `v` for each element) and make the vector `v` reproducible every time I reset the random seed.  
But it turns out that the value of `v` changes for each run in my current code. Do you have any solutions on how to fix this issue?

Thank you!

---

<div class="post-metadata">

### Author: ![skleinbo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/skleinbo/32/36080_2.png) [@skleinbo](https://discourse.julialang.org/u/skleinbo)
#### Post date: [September 7, 2023, 7:17am UTC](https://discourse.julialang.org/t/getting-reproducible-results-in-distributed-computing-with-randomness/103589/2 "2023-09-07T07:17:01Z")

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This seems to be due to [Tasks: don't advance task RNG on task spawn by StefanKarpinski · Pull Request #49110 · JuliaLang/julia · GitHub](https://github.com/JuliaLang/julia/pull/49110)

In the meantime, consider defining an RNG on each worker explicitly, and using that in any random function. They should all accept an RNG as their first argument.

---

<div class="post-metadata">

### Author: ![naonagas](https://avatars.discourse-cdn.com/v4/letter/n/ccd318/32.png) [@naonagas](https://discourse.julialang.org/u/naonagas)
#### Post date: [September 7, 2023, 3:43pm UTC](https://discourse.julialang.org/t/getting-reproducible-results-in-distributed-computing-with-randomness/103589/3 "2023-09-07T15:43:44Z")

</div>

Thank you!  
Putting MersenneTwister in the rand() function seems to provide what I wanted to do.

```julia
using Distributed, SharedArrays

# Start Julia with multiple workers
addprocs()

@everywhere using Random

rng = Vector{MersenneTwister}(undef,nworkers())
for i=1:nworkers()
    rng[i] = MersenneTwister(42+i*1000)
end

v = SharedArray{Float64}(10)
@sync @distributed for i=1:10
    v[i] = rand(rng[myid()-1])
end
println(v)

```