Fix/skip none hyperparameters

sagemaker-train/src/sagemaker/train/common.py

@@ -33,7 +33,7 @@ def __init__(self, options_dict: Dict[str, Any]):
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert back to dictionary for hyperparameters with string values."""
-        return {k: str(getattr(self, k)) for k in self._specs.keys()}
+        return {k: str(v) for k in self._specs.keys() if (v := getattr(self, k)) is not None}
 
     def __setattr__(self, name: str, value: Any):
         if name.startswith('_'):

sagemaker-train/tests/unit/train/evaluate/test_custom_scorer_evaluator.py

@@ -446,7 +446,7 @@ def test_custom_scorer_evaluator_get_custom_scorer_template_additions_with_aggre
 
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.5}
-    mock_extract_options.return_value = {'temperature': {'value': 0.5}}
+    mock_extract_options.return_value = {'temperature': {'default': 0.5}}
 
     evaluator = CustomScorerEvaluator(
         evaluator=DEFAULT_EVALUATOR_ARN,
@@ -610,7 +610,7 @@ def test_custom_scorer_evaluator_evaluate_method(
 
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.7}
-    mock_extract_options.return_value = {'temperature': {'value': 0.7}}
+    mock_extract_options.return_value = {'temperature': {'default': 0.7}}
 
     # Mock Pipeline and execution
     mock_pipeline_instance = Mock()
@@ -673,7 +673,7 @@ def test_custom_scorer_evaluator_evaluate_with_model_package(
 
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.7}
-    mock_extract_options.return_value = {'temperature': {'value': 0.7}}
+    mock_extract_options.return_value = {'temperature': {'default': 0.7}}
 
     # Mock Pipeline and execution
     mock_pipeline_instance = Mock()
@@ -839,8 +839,8 @@ def test_custom_scorer_evaluator_hyperparameters_property(mock_artifact, mock_re
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.7, 'max_new_tokens': 2048}
     mock_extract_options.return_value = {
-        'temperature': {'value': 0.7, 'type': 'float', 'min': 0.0, 'max': 1.0},
-        'max_new_tokens': {'value': 2048, 'type': 'int', 'min': 1, 'max': 8192}
+        'temperature': {'default': 0.7, 'type': 'float', 'min': 0.0, 'max': 1.0},
+        'max_new_tokens': {'default': 2048, 'type': 'int', 'min': 1, 'max': 8192}
     }
 
     evaluator = CustomScorerEvaluator(
@@ -933,7 +933,7 @@ def test_custom_scorer_evaluator_get_custom_scorer_template_additions_builtin(
 
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.7}
-    mock_extract_options.return_value = {'temperature': {'value': 0.7}}
+    mock_extract_options.return_value = {'temperature': {'default': 0.7}}
 
     evaluator = CustomScorerEvaluator(
         evaluator=_BuiltInMetric.PRIME_MATH,
@@ -988,8 +988,8 @@ def test_custom_scorer_evaluator_get_custom_scorer_template_additions_custom_arn
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.5, 'aggregation': 'median'}
     mock_extract_options.return_value = {
-        'temperature': {'value': 0.5},
-        'aggregation': {'value': 'median'}
+        'temperature': {'default': 0.5},
+        'aggregation': {'default': 'median'}
     }
 
     evaluator = CustomScorerEvaluator(
@@ -1048,7 +1048,7 @@ def test_custom_scorer_evaluator_lambda_type_for_nova_models(
 
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.7}
-    mock_extract_options.return_value = {'temperature': {'value': 0.7}}
+    mock_extract_options.return_value = {'temperature': {'default': 0.7}}
 
     # Mock is_nova_model to return True
     mock_is_nova.return_value = True
@@ -1105,7 +1105,7 @@ def test_custom_scorer_evaluator_no_lambda_type_for_non_nova_models(
 
     # Mock recipe utils
     mock_get_params.return_value = {'temperature': 0.7}
-    mock_extract_options.return_value = {'temperature': {'value': 0.7}}
+    mock_extract_options.return_value = {'temperature': {'default': 0.7}}
 
     # Mock is_nova_model to return False
     mock_is_nova.return_value = False

sagemaker-train/tests/unit/train/test_common.py

@@ -0,0 +1,56 @@
+from sagemaker.train.common import FineTuningOptions
+
+
+class TestFineTuningOptionsToDict:
+    """Tests for FineTuningOptions.to_dict() None value handling."""
+
+    def test_to_dict_skips_none_values(self):
+        """None-valued hyperparameters should be omitted from to_dict output."""
+        options = FineTuningOptions({
+            "learning_rate": {"default": 0.0002, "type": "float"},
+            "resume_from_path": {"default": None, "type": "string"},
+            "global_batch_size": {"default": 64, "type": "integer"},
+        })
+        result = options.to_dict()
+        assert "resume_from_path" not in result
+        assert result == {"learning_rate": "0.0002", "global_batch_size": "64"}
+
+    def test_to_dict_includes_non_none_values(self):
+        """Non-None values should be included as strings."""
+        options = FineTuningOptions({
+            "learning_rate": {"default": 0.001, "type": "float"},
+            "max_epochs": {"default": 3, "type": "integer"},
+            "model_name": {"default": "my-model", "type": "string"},
+        })
+        result = options.to_dict()
+        assert result == {
+            "learning_rate": "0.001",
+            "max_epochs": "3",
+            "model_name": "my-model",
+        }
+
+    def test_to_dict_empty_string_is_included(self):
+        """Empty string is a valid value and should not be skipped."""
+        options = FineTuningOptions({
+            "mlflow_run_id": {"default": "", "type": "string"},
+        })
+        result = options.to_dict()
+        assert result == {"mlflow_run_id": ""}
+
+    def test_to_dict_after_user_sets_none_to_value(self):
+        """If user overrides a None default with a real value, it should appear."""
+        options = FineTuningOptions({
+            "resume_from_path": {"default": None, "type": "string"},
+        })
+        options.resume_from_path = "/path/to/checkpoint"
+        result = options.to_dict()
+        assert result == {"resume_from_path": "/path/to/checkpoint"}
+
+    def test_to_dict_all_none_returns_empty(self):
+        """If all values are None, to_dict should return empty dict."""
+        options = FineTuningOptions({
+            "param_a": {"default": None, "type": "string"},
+            "param_b": {"default": None, "type": "string"},
+        })
+        result = options.to_dict()
+        assert result == {}