Moved pytorch changes to pytorch_models to allow for release prep

Author: djm21

src/sasctl/pzmm/write_json_files.py

@@ -470,7 +470,6 @@ def write_file_metadata_json(
         model_prefix: str,
         json_path: Union[str, Path, None] = None,
         is_h2o_model: Optional[bool] = False,
-        is_pytorch_model: Optional[bool] = False,
         is_tf_keras_model: Optional[bool] = False,
     ) -> Union[dict, None]:
         """
@@ -490,10 +489,6 @@ def write_file_metadata_json(
             Sets whether the model metadata is associated with an H2O.ai model. If set
             as True, the MOJO model file will be set as a score resource. The default
             value is False.
-        is_pytorch_model : bool, optional
-            Sets whether the model metadata is associated with an H2O.ai model. If set
-            as True, the .pth file will be set as a score resource. The default
-            value is False.
 
         Returns
         -------
@@ -510,9 +505,6 @@ def write_file_metadata_json(
             dict_list.append({"role": "scoreResource", "name": model_prefix + ".mojo"})
         elif is_tf_keras_model:
             dict_list.append({"role": "scoreResource", "name": model_prefix + ".h5"})
-        # conditions added for pytorch.
-        elif is_pytorch_model:
-            dict_list.append(({"role": "scoreResource", "name": model_prefix + ".pth"}))
         else:
             dict_list.append(
                 {"role": "scoreResource", "name": model_prefix + ".pickle"}

src/sasctl/pzmm/write_score_code.py

@@ -177,7 +177,6 @@ def write_score_code(
             pickle_type,
             mojo_model="mojo_model" in kwargs,
             binary_h2o_model="binary_h2o_model" in kwargs,
-            pytorch_model="pytorch_model" in kwargs,
             tf_model="tf_keras_model" in kwargs or "tf_core_model" in kwargs,
             binary_string=binary_string,
         )
@@ -189,7 +188,6 @@ def write_score_code(
                 model_file_name,
                 pickle_type=pickle_type,
                 mojo_model="mojo_model" in kwargs,
-                pytorch_model="pytorch_model" in kwargs,
                 binary_h2o_model="binary_h2o_model" in kwargs,
             )
         # As above, but for SAS Viya 4 models
@@ -199,7 +197,6 @@ def write_score_code(
                 pickle_type=pickle_type,
                 mojo_model="mojo_model" in kwargs,
                 binary_h2o_model="binary_h2o_model" in kwargs,
-                pytorch_model="pytorch_model" in kwargs,
                 tf_keras_model="tf_keras_model" in kwargs,
                 tf_core_model="tf_core_model" in kwargs,
             )
@@ -408,7 +405,6 @@ def _write_imports(
         mojo_model: Optional[bool] = False,
         binary_h2o_model: Optional[bool] = False,
         tf_model: Optional[bool] = False,
-        pytorch_model: Optional[bool] = False,
         binary_string: Optional[str] = None,
     ) -> None:
         """
@@ -431,9 +427,6 @@ def _write_imports(
         tf_model : bool, optional
             Flag to indicate that the model is a tensorflow model. The default value
             is None.
-        pytorch_model : bool, optional
-            Flag to indicate that the model is a pytorch model. The default value
-            is None.
         binary_string : str, optional
             A binary representation of the Python model object. The default value is
             None.
@@ -482,29 +475,14 @@ def _write_imports(
 import tensorflow as tf
 
             """
-        elif pytorch_model:
-            cls.score_code += "import math\nimport torch\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\n"
-        elif binary_string:
-            cls.score_code += (
-                f'import codecs\n\nbinary_string = "{binary_string}"'
-                f"\nmodel = {pickle_type}.loads(codecs.decode(binary_string"
-                '.encode(), "base64"))\n\n'
-            )
-            """
-import codecs
 
-binary_string = "<binary string>"
-model = pickle.load(codecs.decode(binary_string.encode(), "base64"))
-
-            """
 
     @classmethod
     def _viya35_model_load(
         cls,
         model_id: str,
         model_file_name: str,
         pickle_type: Optional[str] = None,
-        pytorch_model: Optional[str] = None,
         mojo_model: Optional[bool] = False,
         binary_h2o_model: Optional[bool] = False,
     ) -> str:
@@ -561,15 +539,6 @@ def _viya35_model_load(
                 f"{'':8}model = h2o.load(str(Path(\"/models/resources/viya/"
                 f'{model_id}/{model_file_name}")))'
             )
-        elif pytorch_model:
-            cls.score_code += (
-                f'model = torch.load("/models/resources/viya/{model_id}/" + '
-                f"{model_file_name})\n\n"
-            )
-            return (
-                f"{'':8}model = torch.load(\"/models/resources/viya/{model_id}/\" + "
-                f"{model_file_name})\n\n"
-            )
         else:
             cls.score_code += (
                 f'model_path = Path("/models/resources/viya/{model_id}'
@@ -597,7 +566,6 @@ def _viya4_model_load(
         pickle_type: Optional[str] = None,
         mojo_model: Optional[bool] = False,
         binary_h2o_model: Optional[bool] = False,
-        pytorch_model: Optional[bool] = False,
         tf_keras_model: Optional[bool] = False,
         tf_core_model: Optional[bool] = False,
     ) -> str:
@@ -618,9 +586,6 @@ def _viya4_model_load(
         binary_h2o_model : boolean, optional
             Flag to indicate that the model is a H2O.ai binary model. The default value
             is None.
-        pytorch_model : boolean, optional
-            Flag to indicate that the model is a pytorch model. The default value
-            is None.
         tf_keras_model : boolean, optional
             Flag to indicate that the model is a tensorflow keras model. The default
             value is False.
@@ -656,15 +621,6 @@ def _viya4_model_load(
                 f"{'':8}model = h2o.load(str(Path(settings.pickle_path) / "
                 f"{model_file_name}))\n\n"
             )
-        elif pytorch_model:
-            cls.score_code += (
-                f"model = torch.load(Path(settings.pickle_path) / "
-                f"{model_file_name})\n\n"
-            )
-            return (
-                f"{'':8}model = torch.load(Path(settings.pickle_path) / "
-                f"{model_file_name})\n\n"
-            )
         elif tf_keras_model:
             cls.score_code += (
                 f"model = tf.keras.models.load_model(Path(settings.pickle_path) / "

src/sasctl/pzmm/zip_model.py

@@ -33,9 +33,6 @@ def _filter_files(file_dir: Union[str, Path], is_viya4: Optional[bool] = False)
     file_names.extend(sorted(Path(file_dir).glob("*.pickle")))
     # Include H2O.ai MOJO files
     file_names.extend(sorted(Path(file_dir).glob("*.mojo")))
-    # Include Pytorch.ai files
-    file_names.extend(sorted(Path(file_dir).glob("*.pth")))
-    file_names.extend(sorted(Path(file_dir).glob("*.pt")))
     if file_names:
         return file_names
     else: