fix: serverful instance type validations + integ tests

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None

sagemaker-train/src/sagemaker/train/base_trainer.py

@@ -31,6 +31,7 @@
  get_recipe_s3_uri,
  _validate_hyperparameter_values,
  _get_smhp_replicas_enum,
+ _get_smhp_instance_type_enum,
 )
 from sagemaker.train.common_utils.data_utils import validate_data_path_exists
 from sagemaker.train.common_utils.metrics_visualizer import plot_training_metrics
@@ -779,6 +780,22 @@ def _validate_instance_count(self, instance_count, sagemaker_session):
  )
  return smhp_replicas_enum
 
+ def _validate_instance_type(self, instance_type, sagemaker_session):
+ """Validate instance type against allowed values from SMHP recipe."""
+ smhp_instance_type_enum = _get_smhp_instance_type_enum(
+ model_name=self._model_name,
+ customization_technique=self._customization_technique,
+ training_type=self.training_type,
+ sagemaker_session=sagemaker_session,
+ )
+
+ if smhp_instance_type_enum and instance_type not in smhp_instance_type_enum:
+ raise ValueError(
+ f"Instance type '{instance_type}' is not supported. "
+ f"Allowed values: {sorted(smhp_instance_type_enum)}."
+ )
+ return smhp_instance_type_enum
+
  @abstractmethod
  def train(self, input_data_config: List[InputData], wait: bool = True, logs: bool = True, wait_timeout: Optional[int] = None, dry_run: bool = False):
  """Common training method that calls the specific implementation."""
@@ -892,14 +909,30 @@ def _channel_mount_path(dataset_uri, channel_name):
  sagemaker_session=sagemaker_session,
  )
 
+ # Validates instance type using SMHP override spec as SMTJ override spec doesn't contain instance type
+ smhp_instance_type_enum = self._validate_instance_type(compute.instance_type, sagemaker_session)
+ if not smhp_instance_type_enum:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid instance_type enum. "
+ "Instance type validation will be skipped."
+ )
+
  # Validate instance count against allowed values from SMHP recipe.
  smhp_replicas_enum = self._validate_instance_count(compute.instance_count, sagemaker_session)
+
  if smhp_replicas_enum:
  override_spec.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if hasattr(self, 'hyperparameters') and hasattr(self.hyperparameters, '_specs'):
  self.hyperparameters._specs.setdefault("replicas", {})["enum"] = smhp_replicas_enum
  if not hasattr(self.hyperparameters, 'replicas'):
  object.__setattr__(self.hyperparameters, 'replicas', compute.instance_count)
+ else:
+ logger.warning(
+ f"SMHP recipe for {self._model_name}/{self._customization_technique} did not provide a "
+ f"valid replicas enum. "
+ "Instance count validation will be skipped."
+ )
 
  # Inject the resolved dataset channel paths so the rendered recipe's
  # train_files / val_files are non-empty (the container aborts otherwise).

sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py

@@ -1451,10 +1451,45 @@ def _get_smhp_replicas_enum(model_name: str, customization_technique: str, train
  if isinstance(enum_val, list) and enum_val:
  return enum_val
  except Exception as e:
- logger.warning(
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
  f"Could not fetch valid instance counts from SMHP recipe for "
- f"{model_name}/{customization_technique}: {e}. "
- "Instance count validation will be skipped."
+ f"{model_name}/{customization_technique}: {e}."
+ )
+ return None
+
+
+def _get_smhp_instance_type_enum(model_name: str, customization_technique: str, training_type,
+ sagemaker_session, hub_name: Optional[str] = None) -> Optional[list]:
+ """Fetch the instance_type enum from the SMHP override spec for the same model/technique.
+
+ SMTJ hub content does not include an instance_type enum in its override spec, but
+ the SMHP recipe for the same configuration does. This function retrieves that
+ enum so it can be applied to SMTJ recipe validation.
+
+ Returns:
+ List of valid instance types, or None if unavailable.
+ """
+ try:
+ _, smhp_override_spec = _get_recipe_entry_and_override_spec(
+ model_name=model_name,
+ customization_technique=customization_technique,
+ training_type=training_type,
+ sagemaker_session=sagemaker_session,
+ platform="hyperpod",
+ hub_name=hub_name,
+ )
+ instance_type_meta = smhp_override_spec.get("instance_type", {})
+ enum_val = instance_type_meta.get("enum")
+ if isinstance(enum_val, list) and enum_val:
+ return enum_val
+ except Exception as e:
+ # Caller emits the user-facing warning when None is returned; keep the
+ # exception detail at debug level to avoid a duplicate warning.
+ logger.debug(
+ f"Could not fetch valid instance types from SMHP recipe for "
+ f"{model_name}/{customization_technique}: {e}."
  )
  return None
 

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

@@ -18,6 +18,7 @@
 import pytest
 import boto3
 from sagemaker.core.helper.session_helper import Session
+from sagemaker.core.training.configs import TrainingJobCompute
 from sagemaker.train.sft_trainer import SFTTrainer
 from sagemaker.train.common import TrainingType
 
@@ -135,3 +136,32 @@ def test_sft_trainer_nova_workflow(sagemaker_session_us_east_1):
  assert training_job.training_job_status == "Completed"
  assert hasattr(training_job, 'output_model_package_arn')
  assert training_job.output_model_package_arn is not None
+
+
+def test_sft_trainer_lora_invalid_instance_type_raises(sagemaker_session):
+ """An unsupported instance type must raise before a job is submitted.
+
+ Based on the ``test_sft_trainer_lora_workflow`` notebook (Llama LORA on
+ serverful compute in us-west-2). SFTTrainer validates ``instance_type``
+ against the allowed enum from the model's recipe, so ``train()`` should
+ raise a ``ValueError`` rather than launching a training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="meta-textgeneration-llama-3-2-1b-instruct",
+ training_type=TrainingType.LORA,
+ model_package_group="arn:aws:sagemaker:us-west-2:729646638167:model-package-group/sdk-test-finetuned-models",
+ training_dataset="s3://mc-flows-sdk-testing/input_data/sft/sample_data_256_final.jsonl",
+ s3_output_path="s3://mc-flows-sdk-testing/output/",
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for SFT training
+ instance_count=1,
+ ),
+ accept_eula=True,
+ base_job_name=f"sft-lora-integ-bad-type-{unique_id}",
+ sagemaker_session=sagemaker_session,
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)

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

@@ -162,3 +162,68 @@ def test_sft_trainer_serverful_smtj(sagemaker_session_us_east_1, training_resour
  f"{training_job.training_job_status}"
  )
  logger.info(f"Training job completed successfully: {training_job.training_job_name}")
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_type_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance type must raise before a job is submitted.
+
+ SMTJ compute validates ``instance_type`` against the allowed enum from the
+ model's SMHP recipe. ``ml.t3.medium`` is not a valid training instance for
+ Nova, so ``train()`` should raise a ``ValueError`` rather than launching a
+ training job.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.t3.medium", # unsupported for Nova training
+ instance_count=1,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-type-{unique_id}",
+ )
+
+ with pytest.raises(ValueError, match="Instance type 'ml.t3.medium' is not supported"):
+ sft_trainer.train(wait=False, dry_run=True)
+
+
+@pytest.mark.us_east_1
+def test_sft_trainer_serverful_smtj_invalid_instance_count_raises(
+ sagemaker_session_us_east_1, training_resources
+):
+ """An unsupported instance count must raise before a job is submitted.
+
+ Uses a valid instance type so validation reaches the instance-count check,
+ then supplies an out-of-range count. SMTJ compute validates
+ ``instance_count`` against the allowed replicas enum from the model's SMHP
+ recipe, so ``train()`` should raise a ``ValueError``.
+ """
+ unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+
+ invalid_instance_count = 9
+
+ sft_trainer = SFTTrainer(
+ model="nova-textgeneration-lite-v2",
+ training_type=TrainingType.LORA,
+ training_dataset=training_resources["training_dataset"],
+ s3_output_path=training_resources["s3_output_path"],
+ compute=TrainingJobCompute(
+ instance_type="ml.p4d.24xlarge", # valid so count check is reached
+ instance_count=invalid_instance_count,
+ ),
+ sagemaker_session=sagemaker_session_us_east_1,
+ base_job_name=f"sft-smtj-integ-bad-count-{unique_id}",
+ )
+
+ with pytest.raises(
+ ValueError,
+ match=f"Node/Instance count '{invalid_instance_count}' is not supported",
+ ):
+ sft_trainer.train(wait=False, dry_run=True)

sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py

@@ -1425,3 +1425,43 @@ def test_uses_shared_regex_from_reward_verifier(self):
  # Both call sites must share the same compiled pattern, not copies.
  from sagemaker.train.common_utils import rlvr_reward_verifier
  assert fu.LAMBDA_ARN_REGEX is rlvr_reward_verifier.LAMBDA_ARN_REGEX
+
+
+class TestGetSmhpInstanceTypeEnum:
+ """Unit tests for _get_smhp_instance_type_enum (SMHP override-spec enum lookup)."""
+
+ def _call(self):
+ return fu._get_smhp_instance_type_enum(
+ model_name="my-model",
+ customization_technique="SFT",
+ training_type=TrainingType.LORA,
+ sagemaker_session=MagicMock(),
+ )
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_enum_when_present(self, mock_spec):
+ mock_spec.return_value = (
+ {},
+ {"instance_type": {"enum": ["ml.p5.48xlarge", "ml.p4d.24xlarge"]}},
+ )
+ assert self._call() == ["ml.p5.48xlarge", "ml.p4d.24xlarge"]
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_missing(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_instance_type_key_absent(self, mock_spec):
+ mock_spec.return_value = ({}, {})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_when_enum_empty_list(self, mock_spec):
+ mock_spec.return_value = ({}, {"instance_type": {"enum": []}})
+ assert self._call() is None
+
+ @patch.object(fu, "_get_recipe_entry_and_override_spec")
+ def test_returns_none_on_exception(self, mock_spec):
+ mock_spec.side_effect = RuntimeError("hub content unavailable")
+ assert self._call() is None

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

@@ -318,3 +318,44 @@ def test_mlflow_partial_names_defaulted(self):
 
  assert override_spec["mlflow_experiment_name"]["default"] == "user-experiment"
  assert override_spec["mlflow_run_name"]["default"] == "nova-lite-sft"
+
+
+class TestValidateInstanceType:
+ """Unit tests for BaseTrainer._validate_instance_type.
+
+ Validates instance types against the SMHP override-spec enum, and skips
+ validation (returning None) when the enum is unavailable.
+ """
+
+ def _trainer(self):
+ trainer = _ConcreteTrainer.__new__(_ConcreteTrainer)
+ trainer._model_name = "nova-lite"
+ trainer._customization_technique = "sft"
+ trainer.training_type = "lora"
+ return trainer
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_allowed_instance_type_returns_enum(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ result = trainer._validate_instance_type("ml.p4d.24xlarge", MagicMock())
+
+ assert result == ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_disallowed_instance_type_raises(self, mock_enum):
+ mock_enum.return_value = ["ml.p4d.24xlarge", "ml.p5.48xlarge"]
+ trainer = self._trainer()
+
+ with pytest.raises(ValueError, match="is not supported"):
+ trainer._validate_instance_type("ml.g5.xlarge", MagicMock())
+
+ @patch("sagemaker.train.base_trainer._get_smhp_instance_type_enum")
+ def test_skips_validation_when_enum_unavailable(self, mock_enum):
+ """When the enum can't be fetched, validation is skipped (returns None)."""
+ mock_enum.return_value = None
+ trainer = self._trainer()
+
+ # Any instance type is accepted; the method returns None to signal skip.
+ assert trainer._validate_instance_type("ml.g5.xlarge", MagicMock()) is None