Experiment class and search algorithm

[vitobellini]

Hi, I'm using Ray Tune 2.8 to run some HPO but I cannot find a way to specify a searcher algorithm for each experiment.
I want to leverage Repeater and BasicVariantGenerator to run N trials with the same configuration to compute variance in training. Seems like it's not possibile when using Experiment or am I missing something? I need to use Experiment because I want to run multiple experiments in parallel.

I create different experiments such a way:

experiment_spec = Experiment(
                self.name,
                Trainable,
                config=param_space,
                num_samples=self.n_trials,
                stop={"training_iteration": 1},
                trial_name_creator=RayTune.trial_str_creator,
            )

and it run smoothly with:

        _ = run_experiments(
            experiments=experiments,
        )

as confirmed from output

+--------------------------------------------------------+
| Configuration for experiment     test                  |
+--------------------------------------------------------+
| Search algorithm                 BasicVariantGenerator |
| Scheduler                        FIFOScheduler         |
| Number of trials                 2                     |
+--------------------------------------------------------+

[justinvyu]

@vitobellini Could you try using the Tuner API instead?

See below:

• https://docs.ray.io/en/latest/tune/getting-started.html#using-search-algorithms-in-tune
• https://docs.ray.io/en/latest/tune/api/suggestion.html

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