ray.tune.search.nevergrad.NevergradSearch.restore_from_dir#

NevergradSearch.restore_from_dir(checkpoint_dir: str)#

Restores the state of a searcher from a given checkpoint_dir.

Typically, you should use this function to restore from an experiment directory such as /ray_results/trainable.

tuner = tune.Tuner(
    cost,
    run_config=train.RunConfig(
        name=self.experiment_name,
        storage_path="~/my_results",
    ),
    tune_config=tune.TuneConfig(
        search_alg=search_alg,
        num_samples=5
    ),
    param_space=config
)
tuner.fit()

search_alg2 = Searcher()
search_alg2.restore_from_dir(
    os.path.join("~/my_results", self.experiment_name)