fix: alt configs model deployment and training issues

src/sagemaker/jumpstart/artifacts/metric_definitions.py

@@ -96,16 +96,17 @@ def _retrieve_default_training_metric_definitions(
         else []
     )
 
-    instance_specific_metric_name: str
-    for instance_specific_metric_definition in instance_specific_metric_definitions:
-        instance_specific_metric_name = instance_specific_metric_definition["Name"]
-        default_metric_definitions = list(
-            filter(
-                lambda metric_definition: metric_definition["Name"]
-                != instance_specific_metric_name,
-                default_metric_definitions,
+    if instance_specific_metric_definitions:
+        instance_specific_metric_name: str
+        for instance_specific_metric_definition in instance_specific_metric_definitions:
+            instance_specific_metric_name = instance_specific_metric_definition["Name"]
+            default_metric_definitions = list(
+                filter(
+                    lambda metric_definition: metric_definition["Name"]
+                    != instance_specific_metric_name,
+                    default_metric_definitions,
+                )
             )
-        )
-        default_metric_definitions.append(instance_specific_metric_definition)
+            default_metric_definitions.append(instance_specific_metric_definition)
 
     return default_metric_definitions

src/sagemaker/jumpstart/factory/model.py

@@ -259,6 +259,7 @@ def _add_instance_type_to_kwargs(
         sagemaker_session=kwargs.sagemaker_session,
         model_type=kwargs.model_type,
         config_name=kwargs.config_name,
+        hub_arn=kwargs.hub_arn,
     )
 
     if specs.inference_configs and kwargs.config_name not in specs.inference_configs.configs:
@@ -780,6 +781,7 @@ def _add_config_name_to_deploy_kwargs(
             sagemaker_session=temp_session,
             model_type=kwargs.model_type,
             config_name=kwargs.config_name,
+            hub_arn=kwargs.hub_arn,
         )
         default_config_name = _select_inference_config_from_training_config(
             specs=specs, training_config_name=training_config_name

src/sagemaker/jumpstart/hub/parser_utils.py

@@ -14,12 +14,15 @@
 from __future__ import absolute_import
 
 import re
-from typing import Any, Dict
+from typing import Any, Dict, List, Optional
 
 
 def camel_to_snake(camel_case_string: str) -> str:
     """Converts camelCaseString or UpperCamelCaseString to snake_case_string."""
     snake_case_string = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", camel_case_string)
+    if "-" in snake_case_string:
+        #remove any hyphen from the string for accurate conversion.
+        snake_case_string = snake_case_string.replace("-", "")
     return re.sub("([a-z0-9])([A-Z])", r"\1_\2", snake_case_string).lower()
 
 
@@ -29,20 +32,28 @@ def snake_to_upper_camel(snake_case_string: str) -> str:
     return upper_camel_case_string
 
 
-def walk_and_apply_json(json_obj: Dict[Any, Any], apply) -> Dict[Any, Any]:
-    """Recursively walks a json object and applies a given function to the keys."""
+def walk_and_apply_json(
+    json_obj: Dict[Any, Any], apply, stop_keys: Optional[List[str]] = ["metrics"]
+) -> Dict[Any, Any]:
+    """Recursively walks a json object and applies a given function to the keys.
+    stop_keys (Optional[list[str]]): List of field keys that should stop the application function.
+        Any children of these keys will not have the application function applied to them.
+    """
 
     def _walk_and_apply_json(json_obj, new):
         if isinstance(json_obj, dict) and isinstance(new, dict):
             for key, value in json_obj.items():
-                new_key = apply(key)
-                if isinstance(value, dict):
-                    new[new_key] = {}
-                    _walk_and_apply_json(value, new=new[new_key])
-                elif isinstance(value, list):
-                    new[new_key] = []
-                    for item in value:
-                        _walk_and_apply_json(item, new=new[new_key])
+                new_key = apply(key)           
+                if (stop_keys and new_key not in stop_keys) or stop_keys is None:
+                    if isinstance(value, dict):
+                        new[new_key] = {}
+                        _walk_and_apply_json(value, new=new[new_key])
+                    elif isinstance(value, list):
+                        new[new_key] = []
+                        for item in value:
+                            _walk_and_apply_json(item, new=new[new_key])
+                    else:
+                        new[new_key] = value
                 else:
                     new[new_key] = value
         elif isinstance(json_obj, dict) and isinstance(new, list):
@@ -51,6 +62,8 @@ def _walk_and_apply_json(json_obj, new):
             new.update(json_obj)
         elif isinstance(json_obj, list) and isinstance(new, list):
             new.append(json_obj)
+        elif isinstance(json_obj, str) and isinstance(new, list):
+            new.append(json_obj)
         return new
 
     return _walk_and_apply_json(json_obj, new={})

src/sagemaker/jumpstart/types.py

@@ -1174,7 +1174,7 @@ def __init__(self, spec: Optional[Dict[str, Any]], is_hub_content=False):
             spec (Dict[str, Any]): Dictionary representation of training config ranking.
         """
         if is_hub_content:
-            spec = {camel_to_snake(key): val for key, val in spec.items()}
+            spec = walk_and_apply_json(spec, camel_to_snake)
         self.from_json(spec)
 
     def from_json(self, json_obj: Dict[str, Any]) -> None:
@@ -1278,7 +1278,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
             json_obj (Dict[str, Any]): Dictionary representation of spec.
         """
         if self._is_hub_content:
-            json_obj = walk_and_apply_json(json_obj, camel_to_snake)
+            json_obj = walk_and_apply_json(json_obj, camel_to_snake, ["metrics"])
         self.model_id: str = json_obj.get("model_id")
         self.url: str = json_obj.get("url")
         self.version: str = json_obj.get("version")
@@ -1400,7 +1400,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 
         if self.training_supported:
             if self._is_hub_content:
-                self.training_ecr_uri: Optional[str] = json_obj["training_ecr_uri"]
+                self.training_ecr_uri: Optional[str] = json_obj.get("training_ecr_uri")
                 self._non_serializable_slots.append("training_ecr_specs")
             else:
                 self.training_ecr_specs: Optional[JumpStartECRSpecs] = (

src/sagemaker/jumpstart/utils.py

@@ -33,6 +33,7 @@
 
 from sagemaker.jumpstart import constants, enums
 from sagemaker.jumpstart import accessors
+from sagemaker.jumpstart.hub.parser_utils import camel_to_snake, snake_to_upper_camel
 from sagemaker.s3 import parse_s3_url
 from sagemaker.jumpstart.exceptions import (
     DeprecatedJumpStartModelError,
@@ -1103,6 +1104,12 @@ def get_jumpstart_configs(
             metadata_configs.config_rankings.get("overall").rankings if metadata_configs else []
         )
 
+    if hub_arn:
+        return (
+        {config_name: metadata_configs.configs[camel_to_snake(snake_to_upper_camel(config_name))] for config_name in config_names}
+        if metadata_configs
+        else {}
+    )
     return (
         {config_name: metadata_configs.configs[config_name] for config_name in config_names}
         if metadata_configs