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Hey. Are you using ray or optuna?

Hi, I experienced the same behaviour as @A. It always show 1 cpu core is used and training seems to take a long time. I was using Ray on CPU (no access to GPU),

from ray import tune

from ray.tune.search.hyperopt import HyperOptSearch

Thanks.

hey @José Morales, I am using ray as well.

Can you try providing

cpus=1

? Seems like that option controls the resources of each trial, so if you set it to all your cpus (the default) it can just run one trial concurrently

@José Morales Don't really understand what you mean, what is the purpose of 'cpus' then? But it works by setting cpus=1 ☺️

It's used to control the resources that the trials for a model will take. By default ray will try to use all available CPUs when running the optimization, so if you tell it that ModelX needs all of your cpus (by doing

ModelX(cpus=cpu_count())

) then it can only run one trial at a time, whereas if you set

cpus=1

then it can parallelize the trials