Sampling in parallel when a model reads outputs from the file

I have a similar case that is shown in the example here. The only difference if that my linear model reads results from the file

def my_model(theta, x):
    m, c = theta
    y =m * x + c
    np.savetxt('output.out', y, delimiter=',')
    z=np.loadtxt('output.out', delimiter=',', unpack=True)
    return z

so when I sample it in parallel

idata_mh = pm.sample(3000, tune=1000, cores=2)

it is giving me an error

RuntimeError: Chain 0 failed.

I am not sure how to handle parallelization when I need to read model outputs from the same file. I guess, similar to the core number, the number of folders need to be created to handle this process in parallel.

Any help is appreciated!

1 Like

Hi @elchin, I’ll reply here so others can follow the thread.

Assuming that this is a file access problem. If it’s not, please post how the function above ends up in your PyMC model - you’ll need a custom Op if you’re not already doing that.

It sounds like the different processes are colliding the access to that file?
The simplest solution I can think of would be to fetch the thread ID and include it in the file name. (Process IDs may be identical between parent and child on some operating systems if I remember correctly.)

Check this threading — Thread-based parallelism — Python 3.10.4 documentation

Note that the Markov chains must be independent of each other, so if you’re trying to access the same file from all chains: stop it. Best case you’d get terrible performance (file locking) and worst case you could even violate the detailed balance.

Hope this helps, cheers :slight_smile:


Hi Michael,
Thanks for sharing threading with me.
I added these print statements inside my_model.


unfortunately, the numbers are the same.

Multiprocess sampling (2 chains in 2 jobs) 
CompoundStep >Slice: [m] >Slice: [c]


I was thinking about generating a random number and creating a folder corresponding to that random number. Then each process would run in its designated folder. (I just need to make sure that random numbers are unique).