def _process_dataset_param(self) -> None:
"""Dataset needs to be fully executed before sent over to trainables.
A valid dataset configuration in param space looks like:
"datasets": {
"train_dataset": tune.grid_search([ds1, ds2]),
},
"""
execute_dataset(self._param_space)
def test_choice():
ds1 = gen_dataset_func().experimental_lazy().map(lambda x: x)
ds2 = gen_dataset_func().experimental_lazy().map(lambda x: x)
assert not ds1._plan._has_final_stage_snapshot()
assert not ds2._plan._has_final_stage_snapshot()
param_space = {"train_dataset": tune.choice([ds1, ds2])}
execute_dataset(param_space)
executed_ds = param_space["train_dataset"].categories
assert len(executed_ds) == 2
assert executed_ds[0]._plan._has_final_stage_snapshot()
assert executed_ds[1]._plan._has_final_stage_snapshot()
def test_grid_search():
ds1 = gen_dataset_func()._experimental_lazy().map(lambda x: x)
ds2 = gen_dataset_func()._experimental_lazy().map(lambda x: x)
assert not ds1._plan._has_final_stage_snapshot()
assert not ds2._plan._has_final_stage_snapshot()
param_space = {"train_dataset": tune.grid_search([ds1, ds2])}
execute_dataset(param_space)
executed_ds = param_space["train_dataset"]["grid_search"]
assert len(executed_ds) == 2
assert executed_ds[0]._plan._has_final_stage_snapshot()
assert executed_ds[1]._plan._has_final_stage_snapshot()