I understand that the search space API provides a way of e.g. random sampling and grid searching. In my case however, it would be very useful to precisely specify a number of configurations and use tune.run to test all of them for multiple seeds.
Thank you for your reply, it’s great news that this is already possible!
I am not sure I understand how to properly combine both of your suggestions to repeat a number of configurations specified in a list multiple times. A hacky way of doing it would be
from ray import tune
from ray.tune.suggest.basic_variant import BasicVariantGenerator
cfg = [
{"a": 2, "b": 2},
{"a": 1, "b": 1},
{"a": 1, "b": 2}
]
tune.run(
lambda config: config["a"] + config["b"],
config={key: None for key in cfg[0]},
search_alg=BasicVariantGenerator(points_to_evaluate=num_samples * cfg),
num_samples=num_samples * len(cfg))
Is there a cleaner way that doesn’t require multiplying the list of configurations num_samples times?