feature: Accept user-defined env variables for the entry-point

Author: martinRenou

src/sagemaker/model.py

@@ -734,8 +734,8 @@ def _upload_code(self, key_prefix: str, repack: bool = False) -> None:
 
     def _script_mode_env_vars(self):
         """Returns a mapping of environment variables for script mode execution"""
-        script_name = None
-        dir_name = None
+        script_name = self.env.get(SCRIPT_PARAM_NAME.upper(), "")
+        dir_name = self.env.get(DIR_PARAM_NAME.upper(), "")
         if self.uploaded_code:
             script_name = self.uploaded_code.script_name
             if self.repacked_model_data or self.enable_network_isolation():
@@ -751,8 +751,8 @@ def _script_mode_env_vars(self):
                     else "file://" + self.source_dir
                 )
         return {
-            SCRIPT_PARAM_NAME.upper(): script_name or str(),
-            DIR_PARAM_NAME.upper(): dir_name or str(),
+            SCRIPT_PARAM_NAME.upper(): script_name,
+            DIR_PARAM_NAME.upper(): dir_name,
             CONTAINER_LOG_LEVEL_PARAM_NAME.upper(): to_string(self.container_log_level),
             SAGEMAKER_REGION_PARAM_NAME.upper(): self.sagemaker_session.boto_region_name,
         }

tests/unit/sagemaker/training_compiler/test_huggingface_pytorch_compiler.py

@@ -718,3 +718,31 @@ def test_register_hf_pytorch_model_auto_infer_framework(
     sagemaker_session.create_model_package_from_containers.assert_called_with(
         **expected_create_model_package_request
     )
+
+
+def test_accept_user_defined_environment_variables(
+    sagemaker_session,
+    huggingface_training_compiler_version,
+    huggingface_training_compiler_pytorch_version,
+    huggingface_training_compiler_pytorch_py_version,
+):
+    program = "inference.py"
+    directory = "/opt/ml/model/code"
+
+    hf_model = HuggingFaceModel(
+        model_data="s3://some/data.tar.gz",
+        role=ROLE,
+        transformers_version=huggingface_training_compiler_version,
+        pytorch_version=huggingface_training_compiler_pytorch_version,
+        py_version=huggingface_training_compiler_pytorch_py_version,
+        sagemaker_session=sagemaker_session,
+        env={
+            "SAGEMAKER_PROGRAM": program,
+            "SAGEMAKER_SUBMIT_DIRECTORY": directory,
+        },
+    )
+
+    container_env = hf_model.prepare_container_def("ml.m4.xlarge")["Environment"]
+
+    assert container_env["SAGEMAKER_PROGRAM"] == program
+    assert container_env["SAGEMAKER_SUBMIT_DIRECTORY"] == directory