Optimization in serial? about hyperactive

[SimonBlanke]

Hello @esmuigors,

thank you for your kind words! :-)

I suspect this issue does not belong in this repository, because it is a question about the package itself and not about the tutorial (please correct me if I am wrong about this).
Gradient-Free-Optimizers does not have any parallel processing capabilities. Hyperactive can do parallel processing via multiprocessing, joblib and pathos.

So I will transfer this issue to Hyperactive.

[SimonBlanke]

About your question:

Hyperactive will automatically use parallel processing if:

• you set n_jobs > 1
• you added more than 1 search via .add_search(...)

In the first case Hyperactive adds the same search n_jobs-number of times.
In the second case Hyperactive adds different searches.

n_jobs is 1 per default. So you do not need to change this. And you should call .add_search() just once before calling .run(). To not use parallel processing you should write something like this:

# In this case Hyperactive will not use any parallel-processing package to run the optimization. 
# (Even if you use population based optimization algorithms)

...

hyper = Hyperactive()
hyper.add_search(model, search_space)
hyper.run()

...

Does this answer your question? Let me know if this works for your use case.

[esmuigors]

Dear @SimonBlanke

I am very sorry for hastily opening this issue (though it was the opportunity to say those kind words :) ). It turned out to be a problem completely unrelated to either gradient_free_optimizers or the Hyperactive... I didn't look at the modification times of the files, then it became obvious :'-) What a shame on me...

I hope that You answer will be useful for someone else at least :)

Best wishes,
Igors

[SimonBlanke]

Hello @esmuigors,

no Problem! I think the question and answer is still useful for others.
If you have more questions don't hesitate to open another issue.