# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import os
import tarfile
from functools import partial
from typing import Dict, Optional, Union
from toolz import valmap
from gluonts.dataset.common import Dataset, FileDataset, MetaData
from gluonts.model import Predictor
from gluonts.util import safe_extractall
from . import sagemaker
def _strtobool(val):
val = val.strip().lower()
if val in ("y", "yes", "t", "true", "on", "1"):
return True
elif val in ("n", "no", "f", "false", "off", "0"):
return False
else:
raise ValueError(f"invalid truth value {val!r}")
[docs]class TrainEnv(sagemaker.TrainEnv):
def __init__(self, *args, **kwargs):
sagemaker.TrainEnv.__init__(self, *args, **kwargs)
self.datasets = self._load()
def _load(self) -> Dict[str, Union[Dataset, Predictor]]:
if "metadata" in self.channels:
path = self.channels.pop("metadata")
self.hyperparameters["freq"] = MetaData.parse_file(
path / "metadata.json"
).freq
model: Optional[Predictor] = None
if "model" in self.channels:
path = self.channels.pop("model")
with tarfile.open(path / "model.tar.gz") as targz:
safe_extractall(targz, path)
model = Predictor.deserialize(path)
file_dataset = partial(FileDataset, freq=self.hyperparameters["freq"])
datasets = valmap(file_dataset, self.channels)
if self._listify_dataset():
datasets = valmap(list, datasets)
if model is not None:
datasets["model"] = model
return datasets
def _listify_dataset(self):
return _strtobool(self.hyperparameters.get("listify_dataset", "no"))
[docs]class ServeEnv(sagemaker.ServeEnv):
def __init__(self, *args, **kwargs):
sagemaker.ServeEnv.__init__(self, *args, **kwargs)
if self.sagemaker_batch:
from .serve.app import ForecastConfig
self.batch_config = ForecastConfig.parse_raw(
os.environ["INFERENCE_CONFIG"]
)
else:
self.batch_config = None