def temporary_train_env(
hyperparameters: Dict[str, Any],
dataset_name: str,
) -> ContextManager[TrainEnv]:
"""
A context manager that instantiates a training environment from a given
combination of `hyperparameters` and `dataset_name` in a temporary
directory and removes the directory on exit.
Parameters
----------
hyperparameters
The hyperparameters to use when instantiating the
training environment.
dataset_name
The name of the repository dataset to use when instantiating the
training environment.
Returns
-------
ContextManager[gluonts.shell.env.TrainEnv]
A context manager that yields the `TrainEnv` instance.
"""
with tempfile.TemporaryDirectory(prefix="gluonts-train-env") as base:
paths = TrainPaths(base=Path(base))
# write hyperparameters
with paths.hyperparameters.open(mode="w") as fp:
hps_encoded = encode_sagemaker_parameters(hyperparameters)
json.dump(hps_encoded, fp, indent=2, sort_keys=True)
# save dataset
ds_path = materialize_dataset(dataset_name)
path_metadata = paths.data / "metadata" / "metadata.json"
path_train = paths.data / "train"
path_test = paths.data / "test"
path_metadata.parent.mkdir(exist_ok=True)
path_metadata.symlink_to(ds_path / "metadata.json")
path_train.symlink_to(ds_path / "train", target_is_directory=True)
path_test.symlink_to(ds_path / "test", target_is_directory=True)
yield TrainEnv(path=paths.base)
def _materialize(self, directory: Path, regenerate: bool = False) -> None:
materialize_dataset(self._gluonts_name,
directory,
regenerate=regenerate)