Fix: Update model_package_group_name to model_package_group in all trianers to maintain consistency

Author: Roja Reddy Sareddy

sagemaker-train/src/sagemaker/train/dpo_trainer.py

@@ -41,7 +41,7 @@ class DPOTrainer(BaseTrainer):
         trainer = DPOTrainer(
             model="meta-llama/Llama-2-7b-hf",
             training_type=TrainingType.LORA,
-            model_package_group_name="my-model-group",
+            model_package_group="my-model-group",
             training_dataset="s3://bucket/preference_data.jsonl"
         )
 
@@ -50,7 +50,7 @@ class DPOTrainer(BaseTrainer):
         # Complete workflow: create -> wait -> get model package ARN
         trainer = DPOTrainer(
             model="meta-llama/Llama-2-7b-hf",
-            model_package_group_name="my-dpo-models"
+            model_package_group="my-dpo-models"
         )
         
         # Create training job (non-blocking)
@@ -75,7 +75,7 @@ class DPOTrainer(BaseTrainer):
         training_type (Union[TrainingType, str]):
             The fine-tuning approach. Valid values are TrainingType.LORA (default),
             TrainingType.FULL.
-        model_package_group_name (Optional[Union[str, ModelPackageGroup]]):
+        model_package_group (Optional[Union[str, ModelPackageGroup]]):
             The model package group for storing the fine-tuned model. Can be a group name,
             ARN, or ModelPackageGroup object. Required when model is not a ModelPackage.
         mlflow_resource_arn (Optional[str]):
@@ -101,7 +101,7 @@ def __init__(
             self,
             model: Union[str, ModelPackage],
             training_type: Union[TrainingType, str] = TrainingType.LORA,
-            model_package_group_name: Optional[Union[str, ModelPackageGroup]] = None,
+            model_package_group: Optional[Union[str, ModelPackageGroup]] = None,
             mlflow_resource_arn: Optional[str] = None,
             mlflow_experiment_name: Optional[str] = None,
             mlflow_run_name: Optional[str] = None,
@@ -118,8 +118,8 @@ def __init__(
         self.model, self._model_name = _resolve_model_and_name(model, self.sagemaker_session)
         self.training_type = training_type
 
-        self.model_package_group_name = _validate_and_resolve_model_package_group(model,
-                                                                                 model_package_group_name)
+        self.model_package_group = _validate_and_resolve_model_package_group(model,
+                                                                                 model_package_group)
         self.mlflow_resource_arn = mlflow_resource_arn
         self.mlflow_experiment_name = mlflow_experiment_name
         self.mlflow_run_name = mlflow_run_name
@@ -232,7 +232,7 @@ def train(self,
         _validate_hyperparameter_values(final_hyperparameters)
 
         model_package_config = _create_model_package_config(
-            model_package_group_name=self.model_package_group_name,
+            model_package_group_name=self.model_package_group,
             model=self.model,
             sagemaker_session=sagemaker_session
         )

sagemaker-train/src/sagemaker/train/rlaif_trainer.py

@@ -44,7 +44,7 @@ class RLAIFTrainer(BaseTrainer):
         trainer = RLAIFTrainer(
             model="meta-llama/Llama-2-7b-hf",
             training_type=TrainingType.LORA,
-            model_package_group_name="my-model-group",
+            model_package_group="my-model-group",
             reward_model_id="reward-model-id",
             reward_prompt="Rate the helpfulness of this response on a scale of 1-10",
             training_dataset="s3://bucket/rlaif_data.jsonl"
@@ -55,7 +55,7 @@ class RLAIFTrainer(BaseTrainer):
         # Complete workflow: create -> wait -> get model package ARN
         trainer = RLAIFTrainer(
             model="meta-llama/Llama-2-7b-hf",
-            model_package_group_name="my-rlaif-models",
+            model_package_group="my-rlaif-models",
             reward_model_id="reward-model-id",
             reward_prompt="Rate the helpfulness of this response on a scale of 1-10"
         )
@@ -82,7 +82,7 @@ class RLAIFTrainer(BaseTrainer):
         training_type (Union[TrainingType, str]):
             The fine-tuning approach. Valid values are TrainingType.LORA (default),
             TrainingType.FULL.
-        model_package_group_name (Optional[Union[str, ModelPackageGroup]]):
+        model_package_group (Optional[Union[str, ModelPackageGroup]]):
             The model package group for storing the fine-tuned model. Can be a group name,
             ARN, or ModelPackageGroup object. Required when model is not a ModelPackage.
         reward_model_id (str):
@@ -116,7 +116,7 @@ def __init__(
         self,
         model: Union[str, ModelPackage],
         training_type: Union[TrainingType, str] = TrainingType.LORA,
-        model_package_group_name: Optional[Union[str, ModelPackageGroup]] = None,
+        model_package_group: Optional[Union[str, ModelPackageGroup]] = None,
         reward_model_id: str = None,
         reward_prompt: Union[str, Evaluator] = None,
         mlflow_resource_arn: Optional[Union[str, MlflowTrackingServer]] = None,
@@ -138,8 +138,8 @@ def __init__(
         self.model, self._model_name = _resolve_model_and_name(model, self.sagemaker_session)
 
         self.training_type = training_type
-        self.model_package_group_name = _validate_and_resolve_model_package_group(model,
-                                                                                 model_package_group_name)
+        self.model_package_group = _validate_and_resolve_model_package_group(model,
+                                                                                 model_package_group)
         self.reward_model_id = self._validate_reward_model_id(reward_model_id)
         self.reward_prompt = reward_prompt
         self.mlflow_resource_arn = mlflow_resource_arn
@@ -251,7 +251,7 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None, validati
         _validate_hyperparameter_values(final_hyperparameters)
 
         model_package_config = _create_model_package_config(
-            model_package_group_name=self.model_package_group_name,
+            model_package_group_name=self.model_package_group,
             model=self.model,
             sagemaker_session=sagemaker_session
         )

sagemaker-train/src/sagemaker/train/rlvr_trainer.py

@@ -42,7 +42,7 @@ class RLVRTrainer(BaseTrainer):
         trainer = RLVRTrainer(
             model="meta-llama/Llama-2-7b-hf",
             training_type=TrainingType.LORA,
-            model_package_group_name="my-model-group",
+            model_package_group="my-model-group",
             custom_reward_function="arn:aws:sagemaker:us-east-1:123456789012:hub-content/SageMakerPublicHub/JsonDoc/my-evaluator/1.0",
             training_dataset="s3://bucket/rlvr_data.jsonl"
         )
@@ -52,7 +52,7 @@ class RLVRTrainer(BaseTrainer):
         # Complete workflow: create -> wait -> get model package ARN
         trainer = RLVRTrainer(
             model="meta-llama/Llama-2-7b-hf",
-            model_package_group_name="my-rlvr-models",
+            model_package_group="my-rlvr-models",
             custom_reward_function="arn:aws:sagemaker:us-east-1:123456789012:hub-content/SageMakerPublicHub/JsonDoc/my-evaluator/1.0"
         )
         
@@ -78,7 +78,7 @@ class RLVRTrainer(BaseTrainer):
         training_type (Union[TrainingType, str]):
             The fine-tuning approach. Valid values are TrainingType.LORA (default),
             TrainingType.FULL.
-        model_package_group_name (Optional[Union[str, ModelPackageGroup]]):
+        model_package_group (Optional[Union[str, ModelPackageGroup]]):
             The model package group for storing the fine-tuned model. Can be a group name,
             ARN, or ModelPackageGroup object. Required when model is not a ModelPackage.
         custom_reward_function (Optional[Union[str, Evaluator]]):
@@ -108,7 +108,7 @@ def __init__(
         self,
         model: Union[str, ModelPackage],
         training_type: Union[TrainingType, str] = TrainingType.LORA,
-        model_package_group_name: Optional[Union[str, ModelPackageGroup]] = None,
+        model_package_group: Optional[Union[str, ModelPackageGroup]] = None,
         custom_reward_function: Optional[Union[str, Evaluator]] = None,
         mlflow_resource_arn: Optional[Union[str, MlflowTrackingServer]] = None,
         mlflow_experiment_name: Optional[str] = None,
@@ -129,8 +129,8 @@ def __init__(
         self.model, self._model_name = _resolve_model_and_name(model, self.sagemaker_session)
 
         self.training_type = training_type
-        self.model_package_group_name = _validate_and_resolve_model_package_group(model,
-                                                                                 model_package_group_name)
+        self.model_package_group = _validate_and_resolve_model_package_group(model,
+                                                                                 model_package_group)
         self.custom_reward_function = custom_reward_function
         self.mlflow_resource_arn = mlflow_resource_arn
         self.mlflow_experiment_name = mlflow_experiment_name
@@ -239,7 +239,7 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
         _validate_hyperparameter_values(final_hyperparameters)
 
         model_package_config = _create_model_package_config(
-            model_package_group_name=self.model_package_group_name,
+            model_package_group_name=self.model_package_group,
             model=self.model,
             sagemaker_session=sagemaker_session
         )

sagemaker-train/src/sagemaker/train/sft_trainer.py

@@ -42,7 +42,7 @@ class SFTTrainer(BaseTrainer):
         trainer = SFTTrainer(
             model="meta-llama/Llama-2-7b-hf",
             training_type=TrainingType.LORA,
-            model_package_group_name="my-model-group",
+            model_package_group="my-model-group",
             training_dataset="s3://bucket/train.jsonl",
             validation_dataset="s3://bucket/val.jsonl"
         )
@@ -52,7 +52,7 @@ class SFTTrainer(BaseTrainer):
         # Complete workflow:
         trainer = SFTTrainer(
             model="meta-llama/Llama-2-7b-hf",
-            model_package_group_name="my-fine-tuned-models"
+            model_package_group="my-fine-tuned-models"
         )
         
         # Create training job (non-blocking)
@@ -77,7 +77,7 @@ class SFTTrainer(BaseTrainer):
         training_type (Union[TrainingType, str]):
             The fine-tuning approach. Valid values are TrainingType.LORA (default),
             TrainingType.FULL.
-        model_package_group_name (Optional[Union[str, ModelPackageGroup]]):
+        model_package_group (Optional[Union[str, ModelPackageGroup]]):
             The model package group for storing the fine-tuned model. Can be a group name,
             ARN, or ModelPackageGroup object. Required when model is not a ModelPackage.
         mlflow_resource_arn (Optional[str]):
@@ -104,7 +104,7 @@ def __init__(
         self,
         model: Union[str, ModelPackage],
         training_type: Union[TrainingType, str] = TrainingType.LORA,
-        model_package_group_name: Optional[Union[str, ModelPackageGroup]] = None,
+        model_package_group: Optional[Union[str, ModelPackageGroup]] = None,
         mlflow_resource_arn: Optional[str] = None,
         mlflow_experiment_name: Optional[str] = None,
         mlflow_run_name: Optional[str] = None,
@@ -122,8 +122,8 @@ def __init__(
         self.model, self._model_name = _resolve_model_and_name(model, self.sagemaker_session)
         self.training_type = training_type
 
-        self.model_package_group_name = _validate_and_resolve_model_package_group(model,
-                                                                                 model_package_group_name)
+        self.model_package_group = _validate_and_resolve_model_package_group(model,
+                                                                                 model_package_group)
         self.mlflow_resource_arn = mlflow_resource_arn
         self.mlflow_experiment_name = mlflow_experiment_name
         self.mlflow_run_name = mlflow_run_name
@@ -233,7 +233,7 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None, validati
         _validate_hyperparameter_values(final_hyperparameters)
 
         model_package_config = _create_model_package_config(
-            model_package_group_name=self.model_package_group_name,
+            model_package_group_name=self.model_package_group,
             model=self.model,
             sagemaker_session=sagemaker_session
         )

sagemaker-train/tests/integ/train/test_dpo_trainer_integration.py

@@ -29,7 +29,7 @@ def test_dpo_trainer_lora_complete_workflow(sagemaker_session):
     trainer = DPOTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         training_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/dpo-oss-test-data/0.0.1",
         s3_output_path="s3://mc-flows-sdk-testing/output/",
         accept_eula=True
@@ -68,7 +68,7 @@ def test_dpo_trainer_with_validation_dataset(sagemaker_session):
     dpo_trainer = DPOTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         training_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/dpo-oss-test-data/0.0.1",
         validation_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/dpo-oss-test-data/0.0.1",
         s3_output_path="s3://mc-flows-sdk-testing/output/",

sagemaker-train/tests/integ/train/test_rlaif_trainer_integration.py

@@ -28,7 +28,7 @@ def test_rlaif_trainer_lora_complete_workflow(sagemaker_session):
     rlaif_trainer = RLAIFTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         reward_model_id='openai.gpt-oss-120b-1:0',
         reward_prompt='Builtin.Summarize',
         mlflow_experiment_name="test-rlaif-finetuned-models-exp",
@@ -68,7 +68,7 @@ def test_rlaif_trainer_with_custom_reward_settings(sagemaker_session):
     rlaif_trainer = RLAIFTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         reward_model_id='openai.gpt-oss-120b-1:0',
         reward_prompt="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/JsonDoc/rlaif-test-prompt/0.0.1",
         mlflow_experiment_name="test-rlaif-finetuned-models-exp",
@@ -107,7 +107,7 @@ def test_rlaif_trainer_continued_finetuning(sagemaker_session):
     rlaif_trainer = RLAIFTrainer(
         model="arn:aws:sagemaker:us-west-2:729646638167:model-package/sdk-test-finetuned-models/1",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         reward_model_id='openai.gpt-oss-120b-1:0',
         reward_prompt='Builtin.Summarize',
         mlflow_experiment_name="test-rlaif-finetuned-models-exp",

sagemaker-train/tests/integ/train/test_rlvr_trainer_integration.py

@@ -29,7 +29,7 @@ def test_rlvr_trainer_lora_complete_workflow(sagemaker_session):
     rlvr_trainer = RLVRTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         mlflow_experiment_name="test-rlvr-finetuned-models-exp",
         mlflow_run_name="test-rlvr-finetuned-models-run",
         training_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/rlvr-rlaif-oss-test-data/0.0.1",
@@ -67,7 +67,7 @@ def test_rlvr_trainer_with_custom_reward_function(sagemaker_session):
     rlvr_trainer = RLVRTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         mlflow_experiment_name="test-rlvr-finetuned-models-exp",
         mlflow_run_name="test-rlvr-finetuned-models-run",
         training_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/rlvr-rlaif-oss-test-data/0.0.1",
@@ -108,7 +108,7 @@ def test_rlvr_trainer_nova_workflow(sagemaker_session):
     # For fine-tuning 
     rlvr_trainer = RLVRTrainer(
         model="nova-textgeneration-lite-v2",
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         mlflow_experiment_name="test-nova-rlvr-finetuned-models-exp",
         mlflow_run_name="test-nova-rlvr-finetuned-models-run",
         training_dataset="s3://mc-flows-sdk-testing-us-east-1/input_data/rlvr-nova/grpo-64-sample.jsonl",

sagemaker-train/tests/integ/train/test_sft_trainer_integration.py

@@ -29,7 +29,7 @@ def test_sft_trainer_lora_complete_workflow(sagemaker_session):
     sft_trainer = SFTTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+        model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
         training_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/sft-oss-test-data/0.0.1",
         s3_output_path="s3://mc-flows-sdk-testing/output/",
         accept_eula=True
@@ -65,7 +65,7 @@ def test_sft_trainer_with_validation_dataset(sagemaker_session):
     sft_trainer = SFTTrainer(
         model="meta-textgeneration-llama-3-2-1b-instruct",
         training_type=TrainingType.LORA,
-        model_package_group_name="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+        model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
         training_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/sft-oss-test-data/0.0.1",
         validation_dataset="arn:aws:sagemaker:us-west-2:729646638167:hub-content/sdktest/DataSet/sft-oss-test-data/0.0.1",
         accept_eula=True
@@ -103,7 +103,7 @@ def test_sft_trainer_nova_workflow(sagemaker_session):
     sft_trainer_nova = SFTTrainer(
         model="nova-textgeneration-lite-v2",
         training_type=TrainingType.LORA, 
-        model_package_group_name="sdk-test-finetuned-models",
+        model_package_group="sdk-test-finetuned-models",
         mlflow_experiment_name="test-nova-finetuned-models-exp",
         mlflow_run_name="test-nova-finetuned-models-run",
         training_dataset="arn:aws:sagemaker:us-east-1:729646638167:hub-content/sdktest/DataSet/sft-nova-test-dataset/0.0.1",