allow exporting models on remote locations

src/metatrain/cli/export.py

@@ -1,14 +1,13 @@
 import argparse
-import importlib
 import logging
 from pathlib import Path
 from typing import Any, Union
 
 import torch
 
-from ..utils.architectures import check_architecture_name, find_all_architectures
+from ..utils.architectures import find_all_architectures
 from ..utils.export import is_exported
-from ..utils.io import check_file_extension
+from ..utils.io import check_file_extension, load_model
 from .formatter import CustomHelpFormatter
 
 
@@ -41,7 +40,10 @@ def _add_export_model_parser(subparser: argparse._SubParsersAction) -> None:
     parser.add_argument(
         "path",
         type=str,
-        help="Saved model which should be exported",
+        help=(
+            "Saved model which should be exported. Path can be either a URL or a "
+            "local file."
+        ),
     )
     parser.add_argument(
         "-o",
@@ -56,15 +58,14 @@ def _add_export_model_parser(subparser: argparse._SubParsersAction) -> None:
 
 def _prepare_export_model_args(args: argparse.Namespace) -> None:
     """Prepare arguments for export_model."""
-    architecture_name = args.__dict__.pop("architecture_name")
-    check_architecture_name(architecture_name)
-    architecture = importlib.import_module(f"metatrain.{architecture_name}")
-
-    args.model = architecture.__model__.load_checkpoint(args.__dict__.pop("path"))
+    args.model = load_model(
+        architecture_name=args.__dict__.pop("architecture_name"),
+        path=args.__dict__.pop("path"),
+    )
 
 
 def export_model(model: Any, output: Union[Path, str] = "exported-model.pt") -> None:
-    """Export a trained model to allow it to make predictions.
+    """Export a trained model allowing it to make predictions.
 
     This includes predictions within molecular simulation engines. Exported models will
     be saved with a ``.pt`` file ending. If ``path`` does not end with this file

src/metatrain/utils/io.py

@@ -1,6 +1,14 @@
+import importlib
 import warnings
 from pathlib import Path
-from typing import Union
+from typing import Any, Union
+from urllib.parse import urlparse
+from urllib.request import urlretrieve
+
+import torch
+from metatensor.torch.atomistic import check_atomistic_model
+
+from .architectures import check_architecture_name
 
 
 def check_file_extension(
@@ -30,3 +38,32 @@ def check_file_extension(
         return str(path_filename)
     else:
         return path_filename
+
+
+def load_model(architecture_name: str, path: Union[str, Path]) -> Any:
+    """Loads a module from an URL or a local file.
+
+    :param architecture_name: TODO
+    :param path: TODO
+    """
+    if Path(path).suffix in [".yaml", ".yml"]:
+        raise ValueError(f"path '{path}' seems to be option file and not a model")
+
+    if urlparse(str(path)).scheme:
+        path, _ = urlretrieve(str(path))
+
+    try:
+        check_atomistic_model(str(path))
+    except ValueError:
+        check_architecture_name(architecture_name)
+        architecture = importlib.import_module(f"metatrain.{architecture_name}")
+
+        try:
+            return architecture.__model__.load_checkpoint(str(path))
+        except RuntimeError:
+            raise ValueError(
+                f"path '{path}' is not a valid a local or a remote model file for "
+                "the {architecture_name} architecture"
+            )
+    else:
+        return torch.jit.load(str(path))