More shell fixes

- Add proper documentation strings.
- Fix the printed script name in help output.
- Add a `force-static` train parameter that forces the
  creation of a static predictor from the expected
  model location.

src/gluonts/core/log.py

@@ -21,7 +21,7 @@
 DEBUG = os.environ.get('DEBUG', 'false').lower() == 'true'
 logging.basicConfig(level=logging.DEBUG if DEBUG else logging.INFO)
 
-logger = logging.getLogger('SWIST')
+logger = logging.getLogger('gluonts')
 
 
 def metric(metric: str, value: Any) -> None:

src/gluonts/shell/__main__.py

@@ -26,7 +26,7 @@
 from gluonts.model.predictor import Predictor
 
 # Relative imports
-from .sagemaker import SageMakerEnv
+from .sagemaker import TrainEnv, ServeEnv
 
 Forecaster = Type[Union[Estimator, Predictor]]
 
@@ -77,14 +77,39 @@ def cli() -> None:
     type=click.Path(exists=True),
     envvar="SAGEMAKER_DATA_PATH",
     default='/opt/ml',
+    help='The root path of all folders mounted by the SageMaker runtime.',
 )
-@click.option("--forecaster", metavar="NAME", envvar="GLUONTS_FORECASTER")
-def serve_command(data_path: str, forecaster: Optional[str]) -> None:
+@click.option(
+    "--forecaster",
+    metavar='NAME',
+    envvar="GLUONTS_FORECASTER",
+    help=(
+        'An alias or a fully qualified name of a Predictor to use. '
+        'If this value is defined, the inference server runs in the '
+        'so-called dynamic mode, where the predictor is initialized for '
+        'each request using parameters provided in the "configuration" field '
+        'of the JSON request. Otherwise, the server runs in static mode, '
+        'where the predictor is initialized upfront from a serialized model '
+        'located in the {data-path}/model folder.'
+    ),
+)
+@click.option(
+    "--force-static/--no-force-static",
+    envvar="GLUONTS_FORCE_STATIC",
+    default=False,
+    help=(
+        'Forces execution in static mode, even in situations where the '
+        '"forecaster" option is present.'
+    ),
+)
+def serve_command(
+    data_path: str, forecaster: Optional[str], force_static: bool
+) -> None:
     from gluonts.shell import serve
 
-    env = SageMakerEnv(Path(data_path))
+    env = ServeEnv(Path(data_path))
 
-    if forecaster is not None:
+    if not force_static and forecaster is not None:
         serve.run_inference_server(env, forecaster_type_by_name(forecaster))
     else:
         serve.run_inference_server(env, None)
@@ -96,12 +121,24 @@ def serve_command(data_path: str, forecaster: Optional[str]) -> None:
     type=click.Path(exists=True),
     envvar="SAGEMAKER_DATA_PATH",
     default='/opt/ml',
+    help='The root path of all folders mounted by the SageMaker runtime.',
+)
+@click.option(
+    "--forecaster",
+    type=str,
+    envvar="GLUONTS_FORECASTER",
+    help=(
+        'An alias or a fully qualified name of a Predictor or Estimator to '
+        'use. If this value is not defined, the command will try to read it'
+        'from the hyperparameters dictionary under the "forecaster_name" key. '
+        'If the value denotes a Predictor, training will be skipped and the '
+        'command will only do an evaluation on the provided test dataset.'
+    ),
 )
-@click.option("--forecaster", type=str, envvar="GLUONTS_FORECASTER")
 def train_command(data_path: str, forecaster: Optional[str]) -> None:
     from gluonts.shell import train
 
-    env = SageMakerEnv(Path(data_path))
+    env = TrainEnv(Path(data_path))
 
     if forecaster is None:
         try:
@@ -119,4 +156,4 @@ def train_command(data_path: str, forecaster: Optional[str]) -> None:
 
 
 if __name__ == "__main__":
-    cli()
+    cli(prog_name=__package__)

src/gluonts/shell/sagemaker/__init__.py

@@ -19,7 +19,7 @@
 
 from gluonts.dataset.common import FileDataset, MetaData
 from .params import parse_sagemaker_parameters
-from .path import MLPath
+from .path import ServePaths, TrainPaths
 
 
 class DataConfig(BaseModel):
@@ -30,22 +30,21 @@ class DataConfig(BaseModel):
 DATASET_NAMES = 'train', 'test'
 
 
-class SageMakerEnv:
+class TrainEnv:
     def __init__(self, path: Path = Path("/opt/ml")) -> None:
-        self.path = _load_path(path)
+        self.path = TrainPaths(path)
         self.inputdataconfig = _load_inputdataconfig(self.path)
         self.channels = _load_channels(self.path, self.inputdataconfig)
         self.hyperparameters = _load_hyperparameters(self.path, self.channels)
         self.datasets = _load_datasets(self.hyperparameters, self.channels)
 
 
-def _load_path(path: Path) -> MLPath:
-    ml_path = MLPath(path)
-    ml_path.makedirs()
-    return ml_path
+class ServeEnv:
+    def __init__(self, path: Path = Path("/opt/ml")) -> None:
+        self.path = ServePaths(path)
 
 
-def _load_inputdataconfig(path: MLPath) -> Optional[Dict[str, DataConfig]]:
+def _load_inputdataconfig(path: TrainPaths) -> Optional[Dict[str, DataConfig]]:
     if path.inputdataconfig.exists():
         with path.inputdataconfig.open() as json_file:
             return {
@@ -57,7 +56,7 @@ def _load_inputdataconfig(path: MLPath) -> Optional[Dict[str, DataConfig]]:
 
 
 def _load_channels(
-    path: MLPath, inputdataconfig: Optional[Dict[str, DataConfig]]
+    path: TrainPaths, inputdataconfig: Optional[Dict[str, DataConfig]]
 ) -> Dict[str, Path]:
     """Lists the available channels in `/opt/ml/input/data`.
 
@@ -80,7 +79,7 @@ def _load_channels(
         return {channel.name: channel for channel in path.data.iterdir()}
 
 
-def _load_hyperparameters(path: MLPath, channels) -> dict:
+def _load_hyperparameters(path: TrainPaths, channels) -> dict:
     with path.hyperparameters.open() as json_file:
         hyperparameters = parse_sagemaker_parameters(json.load(json_file))
 

src/gluonts/shell/sagemaker/path.py

@@ -16,20 +16,28 @@
 from pathlib import Path
 
 
-class MLPath:
+class TrainPaths:
     def __init__(self, base="/opt/ml") -> None:
         self.base: Path = Path(base).expanduser().resolve()
-        self.config: Path = self.base / "input/config"
-        self.data: Path = self.base / "input/data"
+        self.config: Path = self.base / "input" / "config"
+        self.data: Path = self.base / "input" / "data"
         self.model: Path = self.base / "model"
         self.output: Path = self.base / "output"
 
         self.hyperparameters: Path = self.config / "hyperparameters.json"
         self.inputdataconfig: Path = self.config / "inputdataconfig.json"
 
-    def makedirs(self) -> None:
         self.config.mkdir(parents=True, exist_ok=True)
         self.data.mkdir(parents=True, exist_ok=True)
         self.model.mkdir(parents=True, exist_ok=True)
         self.output.mkdir(parents=True, exist_ok=True)
-        # (self.output / 'data').mkdir(parents=True, exist_ok=True)
+
+
+class ServePaths:
+    def __init__(self, base="/opt/ml") -> None:
+        self.base: Path = Path(base).expanduser().resolve()
+        self.model: Path = self.base / "model"
+        self.output: Path = self.base / "output"
+
+        self.model.mkdir(parents=True, exist_ok=True)
+        self.output.mkdir(parents=True, exist_ok=True)

src/gluonts/shell/serve/__init__.py

@@ -22,7 +22,7 @@
 # First-party imports
 from gluonts.model.estimator import Estimator
 from gluonts.model.predictor import Predictor
-from gluonts.shell.sagemaker import SageMakerEnv
+from gluonts.shell.sagemaker import ServeEnv
 
 import logging
 import multiprocessing
@@ -98,8 +98,7 @@ def stop(self, *args, **kwargs):
 
 
 def run_inference_server(
-    env: SageMakerEnv,
-    forecaster_type: Optional[Type[Union[Estimator, Predictor]]],
+    env: ServeEnv, forecaster_type: Optional[Type[Union[Estimator, Predictor]]]
 ) -> None:
     if forecaster_type is not None:
         ctor = forecaster_type.from_hyperparameters

src/gluonts/shell/train.py

@@ -23,11 +23,11 @@
 from gluonts.transform import Dataset, FilterTransformation, TransformedDataset
 
 # Relative imports
-from .sagemaker import SageMakerEnv
+from .sagemaker import TrainEnv
 
 
 def run_train_and_test(
-    env: SageMakerEnv, forecaster_type: Type[Union[Estimator, Predictor]]
+    env: TrainEnv, forecaster_type: Type[Union[Estimator, Predictor]]
 ) -> None:
     check_gpu_support()