def add_configurations(self,
experiments: Union[Experiment, List[Experiment],
Dict[str, Dict]]):
"""Chains generator given experiment specifications.
Arguments:
experiments (Experiment | list | dict): Experiments to run.
"""
experiment_list = convert_to_experiment_list(experiments)
for experiment in experiment_list:
grid_vals = count_spec_samples(experiment.spec, num_samples=1)
lazy_eval = grid_vals > SERIALIZATION_THRESHOLD
if lazy_eval:
warnings.warn(
f"The number of pre-generated samples ({grid_vals}) "
"exceeds the serialization threshold "
f"({int(SERIALIZATION_THRESHOLD)}). Resume ability is "
"disabled. To fix this, reduce the number of "
"dimensions/size of the provided grid search.")
previous_samples = self._total_samples
points_to_evaluate = copy.deepcopy(self._points_to_evaluate)
self._total_samples += count_variants(experiment.spec,
points_to_evaluate)
iterator = _TrialIterator(uuid_prefix=self._uuid_prefix,
num_samples=experiment.spec.get(
"num_samples", 1),
unresolved_spec=experiment.spec,
output_path=experiment.dir_name,
points_to_evaluate=points_to_evaluate,
lazy_eval=lazy_eval,
start=previous_samples)
self._iterators.append(iterator)
self._trial_generator = itertools.chain(self._trial_generator,
iterator)
def add_configurations(
self,
experiments: Union[Experiment, List[Experiment], Dict[str, Dict]]):
"""Chains generator given experiment specifications.
Arguments:
experiments (Experiment | list | dict): Experiments to run.
"""
experiment_list = convert_to_experiment_list(experiments)
for experiment in experiment_list:
self._total_samples += count_variants(experiment.spec)
self._trial_generator = itertools.chain(
self._trial_generator,
self._generate_trials(
experiment.spec.get("num_samples", 1), experiment.spec,
experiment.name))
