Add support for cache_model_artifact

.github/workflow_scripts/test_cloud.sh

@@ -17,4 +17,4 @@ fi
 
 install_cloud_test
 
-python3 -m pytest -n 2 --junitxml=results.xml tests/unittests/$MODULE/ --framework_version $AG_VERSION
+python3 -m pytest -n 4 --junitxml=results.xml tests/unittests/$MODULE/ --framework_version $AG_VERSION

setup.py

@@ -122,6 +122,7 @@ def default_setup_args(*, version):
     # CLI dependencies (autogluon-cloud command)
     "click>=8.0,<9",
     "rich>=13.0,<15",
+    "huggingface_hub>=0.20,<2",
 ]
 
 extras_require = dict()

src/autogluon/cloud/backend/sagemaker_backend.py

@@ -356,7 +356,8 @@ def deploy(
         wait: bool = True,
         model_kwargs: Optional[Dict] = None,
         deploy_kwargs: Optional[Dict] = None,
-        serve_config: Optional[Dict[str, Any]] = None,
+        fm_serve_config: Optional[Dict[str, Any]] = None,
+        repack: bool = True,
     ) -> None:
         """
         Deploy a predictor as a SageMaker endpoint, which can be used to do real-time inference later.
@@ -397,8 +398,13 @@ def deploy(
         deploy_kwargs:
             Any extra arguments needed to pass to deploy.
             Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.model.Model.deploy for all options
-        serve_config: Optional[Dict[str, Any]], default = None
-            Configuration dict passed to the serve script via the AG_SERVE_CONFIG env var.
+        fm_serve_config: Optional[Dict[str, Any]], default = None
+            Configuration dict passed to the FM serve script via the AG_FM_SERVE_CONFIG env var.
+        repack: bool, default = True
+            Whether the SageMaker SDK should download ``predictor_path``, inject the entry-point script, and re-upload
+            it. Set to False when ``predictor_path`` already contains the serve script (e.g. an artifact bundled by
+            :meth:`FoundationModel.cache_model_artifact`) to skip the round-trip. Ignored when ``predictor_path`` is
+            None.
         """
         assert self.endpoint is None, (
             "There is an endpoint already attached. Either detach it with `detach` or clean it up with `cleanup_deployment`"
@@ -444,19 +450,21 @@ def deploy(
             )
             entry_point = self._serve_script_path
 
-        # Pick model class:
-        # - No artifact → create minimal tarball with serve script (FM deploy)
-        # - Artifact from different source or custom entry point → Repack (inject script into tarball)
-        # - Artifact from fit job, default entry point → NonRepack (script already in tarball)
+        # Pick model class. The question is whether the tarball already contains
+        # the entry_point script — if yes, NonRepack uses it as-is; if no, Repack
+        # injects the script at deploy time.
         if predictor_path is None:
             predictor_path = self._create_serve_script_tarball(entry_point, endpoint_name)
             model_cls = AutoGluonNonRepackInferenceModel
+        elif not repack:
+            model_cls = AutoGluonNonRepackInferenceModel
         else:
-            fit_output = self._fit_job.get_output_path() if self._fit_job is not None else None
-            if predictor_path != fit_output or user_entry_point is not None:
-                model_cls = AutoGluonRepackInferenceModel
-            else:
-                model_cls = AutoGluonNonRepackInferenceModel
+            is_default_fit_output = (
+                self._fit_job is not None
+                and predictor_path == self._fit_job.get_output_path()
+                and user_entry_point is None
+            )
+            model_cls = AutoGluonNonRepackInferenceModel if is_default_fit_output else AutoGluonRepackInferenceModel
 
         # Assemble env vars and deploy
         predictor_cls = self._realtime_predictor_cls
@@ -483,8 +491,8 @@ def deploy(
         else:
             model_kwargs_env = {SAGEMAKER_MODEL_SERVER_WORKERS: "1"}
 
-        if serve_config is not None:
-            model_kwargs_env["AG_SERVE_CONFIG"] = json.dumps(serve_config)
+        if fm_serve_config is not None:
+            model_kwargs_env["AG_FM_SERVE_CONFIG"] = json.dumps(fm_serve_config)
 
         model = model_cls(
             model_data=predictor_path,